Weather, Climate, and Atmospheric Information
This page provides information and opinion on weather, weather forecasting, climate, and atmospheric phenomena
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Antarctic Ice Sheets
Just some information on Antarctic Ice Sheets…
The total Antarctic ice sheet is about about 26-30 million K³. The West Antarctic Ice Sheet (WAIS) is about 3 million K³A and large portions extend into the ocean so it is really sea ice but is connected to the land ice. It is really a Marine Ice Sheet because most of the ice is BELOW sea level. Isostatic adjustments are needed to determine the effect of ice sheet melting on sea level. Some suspect the Antarctic melting is geothermal (i.e. undersea volcanoes) but more research needs to be done. Even if the WAIS completely melted current projections indicate it would only increase sea levels by about 1 – 3.5 meters over several centuries. It is not unusual for the WAIS to melt during interglacials such as we are experiencing. On the other hand, the East Antarctic Ice Sheet is much larger, about 23-26 million K³, and mostly above sea level and is mostly increasing in ice volume, especially in the interior parts. Antarctica contains many sub-glacial lakes created by so much pressure from the massive ice thickness (near 4 km) that the ice reaches the melting point due to pressure known as the pressure melting point (see Second Law of Thermodynamics). In addition, geothermal energy also causes sub-glacial melting. Some research indicates there is erosion of some glaciers in the East Antarctica Ice Sheet though and this is an area of concern because of the sheer size and a greater ability to contribute much more to sea level rise. The Arctic Peninsula Ice Sheet makes up just a fraction of a percent of the total Antarctic ice volume.
Thwaites Glacier, a marine ice sheet and part of the West Antarctic Ice Sheet (WAIS), is unusual in that unlike many glaciers it slopes away from the coast. Circumpolar Deep Water (CDW) is most likely the main cause of the melting. CDW melts glaciers from the bottom. Large portions of the WAIS are below sea level. At the present rate it may melt completely in several hundred years or as long as a millennium. It’s unclear about the consequences of melting but possibly could induce a 3 meter or more sea level rise depending on topographic features and ice dynamics. Melting of the WAIS is not unusual in interglacials.
I’m not an oceanographer or glaciologist but as a meteorologist I find it a fascinating field of study. According to my father’s autobiography, he was involved with Naval Antarctic scientific research expedition (Operation Deep-Freeze USS Arneb, USS Glacier) under Admiral Byrd (just before he died) and spent over a year in Antarctica (1/56 – 2/57). He was at the Little America Ice Station (LAS V) during Navy Operation Deep-Freeze International Geophysical Year (IGY) in the Kainan Bay near the Ross Ice Shelf adjacent to the West Antarctic Ice Sheet. What a desolate place that is!
Here is an interesting concise paper on glacier thermodynamics from UA Fairbanks, one of my alma maters… http://glaciers.gi.alaska.edu/sites/default/files/Notes_thermodynamics_Aschwanden.pdf
Operation Deep-Freeze… https://vimeo.com/356420090
Climate System Non-Linearity
The following is an excerpt from my paper Global Climate Models: Exploring the Reliability, Consistency, Limitations, Deficiencies, Uncertainties, and Methods of Global Climate Models in a Nonlinear and Chaotic Climate System
Unfortunately, virtually all changes in climate and to be more precise all components, processes, and responses within the climate system, are variable and inherently nonlinear. Even processes thought to be linear may be nonlinear when combined with other ongoing dynamics at the time. Nonlinearityproduces many disproportionate changes in the climate system where changes to an independent climate variable (driver) produce unexpected erratic changes to other dependent variables that are impossible to anticipate. Nonlinearity virtually ensures that future changes and outcomes from many climate variables will not conform to past changes. Nonlinearity does not mean you cannot project a future climate but it does ensure that a wide range of variability must not only be considered but expected. Dr. Edward Lorenz, an MIT professor, was instrumental in nonlinear dynamics and chaos theory and coined the term ‘The Butterfly Effect’. You can take a free course on Nonlinear Dynamics through MIT’s OpenCourseWare (knowledge of differential equations is assumed). The IPCC makes this statementon linearity.“Small changes in the climate system can be sufficiently understood by assuming linear relationships between variables. However, many climate processes are non-linear by nature, and conclusions based on linear models and processes may in these cases no longer be valid. Nonlinearity is a prerequisite for the existence of thresholds in the climate system: small perturbations or changes in the forcing can trigger large reorganisations if thresholds are passed. The result is that atmospheric and oceanic circulations may change from one regime to another. This could possibly be manifested as rapid climate change.”
Stocker et al., 2001, pp. 455-456) IPCC Third Assessment Report, Physical Climate Processes and Feedbacks, (TAR, WG-1), Chapter 7.7, pp. 455-456
The following Climatic Change journal articlestated: “In sharp contrast to familiar linear physical processes, nonlinear behavior in the climate results in highly diverse, usually surprising and often counterintuitive observations” (Rial, et al., 2004, p. 12). The weather and ultimately the climate is in a constant state of instability and highly nonlinear where multiple components are interacting with the environment and each other randomly and concurrently. Many components of the climate system are naturally chaotic. The IPCC sums it up this way.“The climate system is particularly challenging since it is known that components in the system are inherently chaotic; there are feedbacks that could potentially switch sign, and there are central processes that affect the system in a complicated, non-linear manner. These complex, chaotic, non-linear dynamics are an inherent aspect of the climate system.”
(Moore III, Gates, Mata, & Underdal, 2001, p. 773) IPCC Third Assessment Report, Advancing Our Understanding, (TAR, WG-1), Chapter 14.2.2, p. 773
Climate model parameterizations are an attempt to simplify and estimate complex and nonlinear processes and resolution issues with what amounts to basically an empirical probability estimate or maybe better described as a ‘best guess’. For simplicity, many times nonlinearities are removed (i.e. made linear) thereby stabilizing chaos and reducing errors. The IPCC stated in the Executive Summary of the Third Assessment Report (TAR) that…“The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible. Rather the focus must be upon the prediction of the probability distribution of the systems future possible states by the generation of ensembles of model solutions. Addressing adequately the statistical nature of climate is computationally intensive and requires the application of new methods of model diagnosis, but such statistical information is essential.”
(Moore III, Gates, Mata, & Underdal, 2001, p. 771) IPCC Third Assessment Report, Advancing Our Understanding, Chapter 14 Executive Summary, p. 771
This paper explains it nicely also:
Abstract. The Earth’s climate system is highly nonlinear: inputs and outputs are not proportional, change is often episodic and abrupt, rather than slow and gradual, and multiple equilibria are the norm. While this is widely accepted, there is a relatively poor understanding of the different types of nonlinearities, how they manifest under various conditions, and whether they reflect a climate system driven by astronomical forcings, by internal feedbacks, or by a combination of both. In this paper, after a brief tutorial on the basics of climate nonlinearity, we provide a number of illustrative examples and highlight key mechanisms that give rise to nonlinear behavior, address scale and methodological issues, suggest a robust alternative to prediction that is based on using integrated assessments within the framework of vulnerability studies and, lastly, recommend a number of research priorities and the establishment of education programs in Earth Systems Science. It is imperative that the Earth’s climate system research community embraces this nonlinear paradigm if we are to move forward in the assessment of the human influence on climate.
Definition of a cloud:
A cloud is a hydrometeor consisting of minute particles of liquid water or ice, or of both, suspended in the atmosphere and usually not touching the ground. It may also include larger particles of liquid water or ice, as well as non-aqueous liquid or solid particles such as those present in fumes, smoke or dust.
Appearance of clouds:
The appearance of a cloud is best described by its dimensions, shape, structure, texture, luminance and colour. These factors are considered below for each of the characteristic cloud forms.
Principles of cloud classification:
Clouds continuously evolve and appear in an infinite variety of forms. However, there is a limited number of characteristic forms frequently observed all over the world, into which clouds can be broadly grouped in a classification scheme. The scheme uses genera, species and varieties. This is similar to the systems used in the classification of plants or animals, and similarly uses Latin names.
There are some intermediate or transitional forms of clouds that, although observed fairly frequently, are not described in the classification scheme. The transitional forms are of little interest; they are less stable and in appearance are not very different from the definitions of the characteristic forms.
There also two additional cloud classifications: Special clouds and Upper atmospheric clouds. These tend to be only rarely or occasionally observed and, in some cases, only in certain parts of the world.
Note: These cloud links will open in the current tab. You will lose your place when you try to return to the current page unless you explicitly open them in a new tab.
Source: WMO International Cloud Atlas
See WMO International Cloud Atlas for more information
Conditional Symmetric Instability
Conditional Symmetric Instability:
• Typically a cool season phenomenon
• Wind profile: speed increasing with height with weak directional
veering with height; indicative of strong baroclinicity
• Thermodynamic profile: nearly saturated and close to the moist adiabatic lapse rate. Parcel motion will be neutral to moist ascent.
Lapse rate is NOT conditionally unstable
• Often found in the vicinity of an extratropical cyclone warm front,
ahead of a long-wave trough in a region of strong, moist, midtropospheric southwesterly flow
• CSI also is possible near entrance regions of anticyclonically
curved jet streaks where inertial and gravitational stability is weak
or neutral; however, be aware that gravitational instability is
possible in these areas which could lead to upright convection with
thunder and lightning (especially south of a CSI area)
• Large scale forcing for upward vertical motion usually is present
• Soundings reveal a deep, moist layer that is convectively stable with
a moist-adiabatic lapse rate
• On satellite or radar imagery, CSI is exhibited by multiple bands of
clouds/precipitation oriented parallel to the mid-tropospheric
thermal wind (or thickness lines); sometimes the bands have a
component of motion toward the warm air
• These heavier precipitation bands may be embedded (obscured) by
other lighter precipitation
• Warm frontal rain/snow bands are often good candidates for being
associated with CSI
Dedritic Growth Zone
The dendritic growth zone (DGZ), is a layer in the atmosphere where the temperature is between 12 and 18 below zero Celsius. This is sometimes expanded to 10-20 below zero Celsius. The layer is often used to determine the size and shape of snow crystals that are forming. Determination of the size, shape, and volume (condensed or fluffy due to trapping of air) can aid in forecasting the amount of snowfall. Fluffy snowflakes will create more snowfall accumulation than condensed snowflakes despite having the same water equivalent.https://www.its.caltech.edu/~atomic/snowcrystals/class/class-old.htm https://lukemweather.blogspot.com/2010/12/finallyhow-do-we-calculate-snow-ratios.html
Does warm air ‘hold’ more moisture?
While there is a correlation the implication is that warm air has more room to include water vapor, which is incorrect. A complete understanding of thermodynamics, and atmospheric physics and chemistry helps to understand the processes. The temperature of the water vapor itself is much more important than the air around it. The saturation vapor pressure is higher at higher temperatures but the air itself does not “hold” water vapor. It’s simply a bad science layman’s and even some educator’s simplified explanation of a more complex process that has been perpetuated and ignores the reality of the processes that are really happening. At first, I used the simplified explanation but during several years of mid and upper atmospheric chemistry and physics research (aeronomy), I abandoned the simple explanation early on not only because it wasn’t scientifically complete and accurate but it doesn’t properly describe or explain the atmospheric thermodynamic, physical, and chemical processes that are occurring and need to be precisely detailed in research papers. But…to each his own if it helps you to more easily understand the process through that correlation, just realize and be aware that the real science behind it is a bit more complicated.
Warm air and the corresponding warm water vapor exist together in the atmosphere. The temperature of the air does affect the temperature of the microscopic droplets in water vapor so there is a correlation but the terminology of “holding” water vapor is my point, which is misleading. The correlation just enhances a simpler understanding. Air and water vapor interact according to laws of partial pressure (Dalton’s Law). Air does not “hold” water vapor. They exist together and share the same volume at nearly the same temperature.
The air doesn’t have a temperature-dependent holding capacity for water vapor although there is a correlation.
Droughts (California and western U.S.)
It’s not unusual at all for California and western US to have droughts and extreme climate variability. California has a long history of droughts. El Nino (wet) or La Nina (dry) and atmospheric blocking weather patterns that can be persistent. In addition, ocean/atmospheric interaction of the Pacific Decadal Oscillation (PDO), Madden-Julian Oscillation (MJO) and Atlantic Multidecadal Oscillation (AMO) can have a significant impact in the western part of the US. We have been lucky the last 500 years with alternating periods of drier and wetter than normal conditions which have usually lasted 25 years or less. Between 800 and 1300 AD we had centuries of drought there. While it is not unusual for drought in the California, Nevada and Southwest it sure is uncomfortable and troubling for the folks that live there. In addition, the population has exploded there over the last century putting a strain on their resources. California has a long history of droughts. El Nino (wet) or La Nina (dry) and atmospheric blocking weather patterns that can be persistent. In addition, ocean/atmospheric interaction of the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) can have a significant impact in the western part of the US. We have been lucky the last 500 years with alternating periods of drier and wetter than normal conditions which have usually lasted 25 years or less. Between 800 and 1300 AD we had centuries of drought there. While it is not unusual for drought in the California, Nevada and Southwest it sure is uncomfortable and troubling for the folks that live there.
Glossary of National Hurricane Center (NHC )Terms: https://www.nhc.noaa.gov/aboutgloss.shtml
Information on Hurricanes:
Although I have a general and specialized education on tropical systems, including tropical cyclones and their causes and effects, I do not have an in depth working knowledge and application of tropical meteorology including tropical cyclones nor do I claim to be an expert on tropical and subtropical systems, cold core/warm core systems nor developing systems in either a barotropic or baroclinic atmosphere. On the contrary, my expertise lies more with research in desert and cold weather/arctic atmospheric profiling.
Hurricanes are very difficult to monitor and track with complete accuracy. Even with the equipment we have today some assumptions, parameterizations, and a thorough analysis of many factors are still needed to display the information gained from reconnaissance missions in a useful way. There are several different ways to measure wind speeds in a hurricane. Originally and still today for tropical depressions and storms, data is collected by aircraft at the 700 MB level (~10,000 feet) and surface wind speeds are calculated to determine a surface wind speed from various ratios derived from past empirical data. This is surely not an exact method but does provide an “in the ballpark” estimate of surface wind speeds especially as storms (cyclones) are developing and strengthening. Normally an estimation of 80-90% of the 700 MB wind speed is used. As tropical storms intensify into hurricanes GPS dropsondes and WP3D Stepped Frequency Microwave Radiometer (SFMR) are used. While dropsondes are very accurate (within several MPH) they are expensive and only about 15-20 are used during any Hurricane Hunters mission leaving many unobserved areas to be filled in by expert severe weather analysis teams. SFMR uses certain frequencies to measure microwave radiation from the ocean surface. Using some assumptions about the atmosphere they gather what is called an ocean surface brightness temperature and then a calculation of wind speeds can be obtained from a linear assumption of wind speed and corresponding brightness. In high wind scenarios these methods don’t always give the same results although they are usually fairly similar. Severe weather experts must determine, for that particular storm, what represents the most accurate measurement based on a number of parameters and most importantly the internal dynamics of the storm that are constantly changing. There are parametric and dynamical approaches to forecast a wind distribution field in hurricanes. There are the data intensive dynamical models the National Hurricane Center prefers and several parametric models (each has their benefits depending on the storm) that can quickly forecast a wind distribution field almost as well as the dynamical approach and much more quickly.
The Cyclone Global Navigation Satellite System (CYGNSS) was launched in December 2016 to measure reflected GPS satellite signals from low orbit to monitor storm wind speeds from space. Hurricanes are very difficult to monitor and track with complete accuracy. The following applies in every instance as it pertains to winds in a hurricane (Northern Hemisphere):
- The strongest winds in most areas of a hurricane are normally found at about 300-500 meters ABOVE the surface unaffected by surface friction.
- Winds officially reported as sustained winds are obtained by several methods, a 10 minute average or one, sometime two, minute average and are measured at ~33 feet (10.1 meters) ABOVE the ground in an unobstructed location.
- The strongest winds in a hurricane are found in the right side, especially the right forward quadrant (assuming you are in the northern hemisphere).
- The winds in the right side are not only stronger but extend much farther out as does the entire field of tropical storm winds (winds >39 MPH).
- The strongest winds in the left quadrant are about 20% and possibly 25% weaker than the right side.
- The winds in the left side diminish much quicker as you move away from the eye wall.
- The strongest winds normally occur about 15 miles (can occur between 6 and 30 miles) from the center of the eye.
- Winds can decrease up to 30% in just 15 miles (if the max winds are 6 miles from the center of the eye) and by more than 40% just 30 miles from the center of the eye, especially as it applies to the left side where winds diminish more quickly with distance.
- Winds can drop by 50% or more just 60 miles from the center of the eye and at 60-70 miles from the center can be just 40-50% of the maximum winds for that quadrants eye wall, especially as it applies to the left side where winds diminish more quickly with distance.
- Other meteorological parameters, wave height and direction, coastal geographical features etc. can affect wind speeds dramatically, especially as a storm moves over land (friction), loses strength due to wind shear, dry air, cooler water etc. and a number of other factors and begins or continues to deteriorate.
Finally, while estimates can be made it is impossible to determine the dynamics and resulting effects in every area covered by the hurricane. Each hurricane is unique unto itself and its properties are affected by forces internally and externally. Wind speeds are ever changing with the internal dynamics and external forces and the sampling of data covers very small portions of the hurricane leaving experts to “fill in the blanks”. As stated previously there are also many algorithms used based not only on physical data but also on assumptions and parameterizations. NOAA (National Hurricane Center) will almost always use a dynamical approach when tracking and forecasting hurricanes through use of dynamical atmospheric and ocean models. These models can used independently (i.e. GFS, ECMWF) or coupled/linked with an ocean model (i.e. GFDL, GFDN, HWRF) to better represent ocean/atmospheric interaction
IPCC Extreme Events Summary
Here are some summary excerpts from the latest IPCC report, IPCC AR5 WGI Chapter 2 extreme events:
“Current datasets indicate no significant observed trends in global tropical cyclone frequency over the past century … No robust trends in annual numbers of tropical storms, hurricanes and major hurricanes counts have been identified over the past 100 years in the North Atlantic basin” “In summary, there continues to be a lack of evidence and thus low confidence regarding the sign of trend in the magnitude and/or frequency of floods on a global scale.” “In summary, there continues to be a lack of evidence and thus low confidence regarding the sign of trend in the magnitude and/or frequency of floods on a global scale” “In summary, there is low confidence in observed trends in small-scale severe weather phenomena such as hail and thunderstorms because of historical data inhomogeneities and inadequacies in monitoring systems” “In summary, the current assessment concludes that there is not enough evidence at present to suggest more than low confidence in a global-scale observed trend in drought or dryness (lack of rainfall) since the middle of the 20th century due to lack of direct observations, geographical inconsistencies in the trends, and dependencies of inferred trends on the index choice. Based on updated studies, AR4 conclusions regarding global increasing trends in drought since the 1970s were probably overstated. However, it is likely that the frequency and intensity of drought has increased in the Mediterranean and West Africa and decreased in central North America and north-west Australia since 1950” Atmospheric Circulation: “In summary, confidence is low in changes in surface wind speed over the land and over the oceans owing to remaining uncertainties in data sets and measures used.” “In summary, upper-air winds are less studied than other aspects of the circulation, and less is known about the quality of data products, hence confidence in upper-air wind trends is low.” “In summary, large interannual-to-decadal variability is found in the strength of the Hadley and Walker circulation. The confidence in trends in the strength of the Hadley circulation is low due to uncertainties in reanalysis data sets. Recent strengthening of the Pacific Walker circulation has largely offset the weakening trend from the 19th century to the 1990s (high confidence). Several lines of independent evidence indicate a widening of the tropical belt since the 1970s. The suggested weakening of the East Asian monsoon has low confidence, given the nature and quality of the evidence.” WG1AR5_Chapter 02 (Chapter 2.6 starts on page 208)… https://www.ipcc.ch/site/assets/uploads/2017/09/WG1AR5_Chapter02_FINAL.pdf
Description of Meteorological and Climatological Functions
“Blind belief in authority is the greatest enemy of truth.” – Albert Einstein
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Meteorological Instruments Used
Meteorological Instruments Used:Sling Psychrometer https://glossary.ametsoc.org/wiki/Sling_psychrometer Net Exchange Radiometers (all waves) https://eko-eu.com/products/environmental-science/pyrgeometers Total Hemispheric Radiometer (all wavelengths) https://journals.ametsoc.org/view/journals/apme/4/1/1520-0450_1965_004_0112_aithr_2_0_co_2.xml?tab_body=fulltext-display Eppley Pyranometer (short wave radiation) http://www.eppleylab.com/instrument-list/standard-precision-pyranometer/ Pyrgeometers (long-wave downward radiation) https://eko-eu.com/products/environmental-science/pyrgeometers Normal Incidence Pyreheliometer (short wave radiation) http://www.eppleylab.com/instrument-list/normal-incidence-pyrheliometer/ Eppley Pyreheliometer (short wave radiation) Light bulb type is now obsolete. Solar Radiation Measuring Set (short wave radiation) http://meteorologyequipment.tpub.com/TM-750-5-3/TM-750-5-30025.htm Mast Ozone Meter 724-2: Ozone sensing and measuring instrument. General Characteristics Item Description: Microcoulomb sensor, 0 to 100 pphm/vol range, direct readout dial, 140 cc/min. sampling rate, 115 vac 60 Hz, 12 W power, 7.5 in. W, 6.0 in. D, 11.5 in. HWS101 Wind Set: Measures wind speed and direction. Set is used for studies, turbulent diffusion, air pollution and micromet surveys. UVW Wind Systems http://www.webmet.com/met_monitoring/222.html Anemometers and Laser Anemometer (LDA) https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1028&context=provost_schol Thermograph and Hygrothermograph https://en.wikipedia.org/wiki/Thermo-hygrograph Barograph and Microbarograph https://www.infoplease.com/encyclopedia/earth/weather/meteorology/barograph Aneroid Barometer http://belfortinstrument.com/ambient-meteorological/pressure/aneroid-barometer/ Mercury barometer http://hyperphysics.phy-astr.gsu.edu/hbase/pman.html Thermistor https://www.ametherm.com/thermistor/thermistor-applications Mechanical Weather Station Model 1071-1072: A four parameter weather station measuring wind speed, wind direction, temperature, and rainfall with a recorder capable of recording for 32-65 days depending on the set chart speed. Recorder can be hand wound or operated with a battery. Theodolite https://www.infoplease.com/encyclopedia/earth/weather/meteorology/theodolite Ground Meteorological Device (GMD) – Tracks and receives upper air sounding data http://meteorologyequipment.tpub.com/TM-750-5-3/TM-750-5-30015.htm Radiosonde https://www.weather.gov/upperair/factsheet http://meteorologyequipment.tpub.com/TM-750-5-3/TM-750-5-30013.htm Various Weather Radars (T9, WSR74C) ARCAS Meteorological Rocket http://www.astronautix.com/a/arcas.html Loki/Super Loki Meteorological Rocket http://www.astronautix.com/l/loki.html http://www.astronautix.com/s/superloki.html I’m sure there are more and will add as I remember them.
Severe Weather Parameters
Standard Atmosphere:Troposphere: Layer nearest the earth (~0-12 km); Lapse Rate > 0 (temperature decreases with height); heat source is earth’s surface; ends at tropopause.
Note: The tropopause is lower at the poles (about 8 km) and higher at the equator (about 18 km) and varies by season.Stratosphere: Layer above Tropopause (~12-50 km); Lapse Rate < 0 (temperature increases with height); Heat source O3 (ozone); Polar Stratospheric Clouds (PSCs), also called nacreous clouds can be observed in the lower part of this layer; ends at stratopause; https://scied.ucar.edu/shortcontent/stratosphere-overview
Mesosphere: Layer above Stratopause (~50-85 km); Lapse Rate > 0 (temperature decreases with height); no heat source; contains coldest atmospheric temperature ~-90C; very little water vapor; area for noctilucent clouds; ends at mesopause.Thermosphere: Layer above Menopause (~85-500 km); Lapse Rate < 0 (temperature increases with height); heat due to ionization; very thin atmosphere and very sensitive to solar activity; no water vapor; temperatures can reach over 1500C; includes the chemical ionosphere; most auroral activity here. https://www.nrl.navy.mil/news/releases/nasa-confirms-icon-mission-nrls-mighti-prepares-flight https://icon.ssl.berkeley.edu/Instruments/MIGHTI Exosphere: The topmost layer of the thermosphere is the thermopause or sometime referred to as the exobase where atoms and molecules begin escaping into space about 250-500 km; the gas laws no longer apply; exists at altitude of ~500-1000 km. Note: The ‘F’ region of the ionosphere can extend into the Exosphere.
Other Notable regions of the Atmosphere (not technically layers):
Ozonosphere: A region of the atmosphere ~12-50 km (most ozone concentrated ~15-25 km) within the stratosphere containing most of the earth’s ozone. Altitude varies with season and geography.Ionosphere: A region of the atmosphere (~80-550 km) within the mesosphere, thermosphere, and parts of the exosphere that is ionized by solar radiation. Most auroral activity occurs here at 100-200 km. Note: The ‘D’ region of the ionosphere can be in the mesosphere and lower thermosphere.
Contains 3 distinct layers defined by radiation wavelength:
‘D’ region peak is ~90 km but can vary from 50-140 km; strongly absorbs AM radio waves; region disappears at night and becomes reflective; absorbs x-rays.
‘E’ region is ~110 km but can vary from ~95-145 km; strongly reflects AM radio waves; absorbs x-rays.‘F’ region is ~145-600 km; Extreme Ultraviolet Radiation (EUV is) absorbed. ‘F1’ region optimum is ~145-200 km; vanishes at night.
‘F2’ region optimum is ~200-400 km, usually about 300 km during the day and higher at night.
“Who is more humble? The scientist who looks at the universe with an open mind and accepts whatever the universe has to teach us, or somebody who says everything in this book must be considered the literal truth and never mind the fallibility of all the human beings involved?” – Carl Sagan
Weather and Climate Modification (discussion and opinion)
Discussion and Opinion:
I can say that weather and climate modification are a part of DOD’s military operations and defense strategy and have been for many years. Environmental Modification (ENMOD) techniques continue to be evaluated through use of various traditional and nontraditional weather modification strategies, including upper atmospheric studies (i.e. aeronomy and “space weather”) involving ionification of the atmosphere. “Weather warfare” involving things like cloud seeding, generating fog, delaying or forcing precipitation, interrupting communications, natural, agricultural, and economic disruption, and the possible manipulation of weather patterns and more are just some of the areas that are in play currently or could be in the near future. There are also untapped weather modification methods being researched much the same way as new weapon systems are and the consequences are potentially catastrophic for our enemies. HAARP was very real and was a vital part of the Strategic Defense Initiative (i.e. Star Wars) that began under the Reagan administration. The Air Force calls it “Owning the Weather”. Although the HAARP project has been officially terminated by the Air Force, the scientific initiative lives on as it was transferred to the University of Alaska Geophysical Institute, Fairbanks in 2015. HAARP has the potential to create not only weather chaos but atmospheric chaos. In my opinion, limited micro-scale and mesoscale disruption is currently possible. The ability to modify climate and weather is a reality and a significant and powerful defense strategy that in its full capacity may be used in many ways as weapons of war with no deployment of troops or equipment. The exact extent of these modification techniques are still being researched and developed. I suspect great advancements in this area are being developed far beyond what most of us could imagine. Let me be perfectly clear. In my opinion, weather and climate modification and weather warfare are real, powerful, and potentially deadly weapons which eventually, if they are ever developed to their maximum potential, may be able to be used on a global basis on anyone without their knowledge. The real question is… should they?
Although I participated in many research projects at the Poker Flat Research Range run by the University of Alaska Geophysical Institute in Fairbanks, AK many years ago, some of which were indirectly related to environmental modification, I am not at liberty to discuss specific details of any related weather modification initiatives, specifically, research and development regarding upper atmospheric ionization research and the potential effects on the weather or climate, short term or long.Sources: https://www.gi.alaska.edu/facilities/haarp https://apps.dtic.mil/docs/citations/ADA333462 https://www.globalresearch.ca/does-the-us-military-own-the-weather-weaponizing-the-weather-as-an-instrument-of-modern-warfare/5608728 https://www.geoengineeringwatch.org/environmental-modification-techniques-enmod-and-climate-change/
Weather Observations and Satellite Data for Climate Studies
The following is an excerpt from my paper Global Climate Models: Exploring the Reliability, Consistency, Limitations, Deficiencies, Uncertainties, and Methods of Global Climate Models in a Nonlinear and Chaotic Climate System
Weather Observations and Satellite Data for Climate Studies
Although I recognize that anthropogenic forcing is likely causing warming, I currently tend to agree with lower to mid range of the IPCC scenarios (.1-.15 °C /decade) for surface temperature in the immediate future, which matches well with the satellite observations since 1979, with continued interruptions caused mostly by climate oscillations or other natural causes. Now that many, but certainly not all, of the satellite data inaccuracies have been mostly resolved, I prefer to use the satellite data (RSS/UAH) of lower tropospheric (TLT) and mid tropospheric (TMT) temperature (lower atmospheric temperature from ~1-10 km) for climatic temperature trends. I prefer TLT, which eliminates any stratospheric influence, or the adjusted TMT, which removes the stratospheric influence. I realize that is not where we live but there are numerous faults with surface observations that I will expound upon later.
Overall the UAH and RSS TLT temperature data is remarkably similar with RSS being, on average just slightly cooler than UAH data before the most recent version. Recent versions of UAH V6 (Mar 2015) and RSS 4 (Jun 2016) of TLT data now show that RSS V4 TLT is slightly warmer than UAH V6 TLT. These differences are driven mostly by satellite orbit and diurnal drift corrections. Before the latest versions, the UAH data had been slightly warmer in the TLT while the RSS data has been slightly warmer in the TMT. It’s somewhat ironic that they have virtually changed trends over the last decade displayed by this graph (Stokes, 2017), Now, the UAH is becoming cooler and the RSS warmer. The new UAH V6 is more similar to the older RSS V3.3 while the older UAH V5.6 is now more similar but not as high as RSS V4. The GCMs are particularly aggressive with temperature increases when compared to observed satellite and balloon data of the lower troposphere.
Recent adjustments (UAH V6 Mar 2015) to satellite data show a slightly lower tropospheric temperature (TLT) reducing the decadal trend from .14 °C /decade to .114 °C /decade (Spencer, Christy, & Braswell, 2015) mainly due to high spatial resolution and drift corrections. UAH V6 (vs. V5.6) regional temperature trends (Christy & Spencer, 2015) increased warming slightly in the tropics and Southern Hemisphere while decreasing warming slightly over the Northern Hemisphere including the Arctic. The Antarctic remained virtually unchanged.
Currently, satellite data (RSS/UAH) from 2016 indicates a trend (National Oceanic and Atmospheric Administration [NOAA], 2016) of .14-.16 °C /decade when including the recent El Nino; otherwise, the previous trend (NOAA, 2015) is .12-.14 °C /decade. UAH and RSS TLT satellite measurements since 1979 indicate warming of ~.114-.135 °C/decade while surface datasets (Simmons et al., 2017, p. 5) indicate warming of ~.16-.18 °C/decade over that time, thus ~25% less warming than surface temperature datasets. From 1998-2014 the satellite data was fairly flat.
A much maligned bulk atmospheric comparison (Christy, 2017), p. 5 Figure 2, p. 6 Figure 3, of satellite and balloon datasets shows a significantly less rate of total tropospheric warming relative to the rate of model projected surface warming. As you can see from the graphs on this page (Remote Sensing Systems, n.d.) the lower tropospheric temperature (TLT) from RSS, Fig. 1., identified by the thick black line continues to remain fairly steady or increasing just slightly over the last 15-20 years while the cumulative model simulation (33 CMIP-5 simulations in his case), identified by the yellow uncertainty band continues to escalate. By 2015 the satellite data is just barely within the very lowest part of the simulated uncertainty band. The difference was even greater with RSS 3.3 before the adjustments Fig. 1. (Remote Sensing Systems [Archives], n.d.). These figures from RSS seem to somewhat confirm the bulk atmospheric temperature graph by Christy above. The balloon datasets trend is even lower than the satellite thick black line in Fig. 1. RSS issued the following statement on the graph in Fig. 1. “The troposphere has not warmed quite as fast as most climate models predict. Note that this problem has been reduced by the large 2015-2016 El Nino Event and the updated version of the RSS tropospheric datasets.” (Remote Sensing Systems, n.d.)
Stratospheric cooling and ozone depletion is another area where models have significant problems. Lower stratospheric cooling (TLS) has actually been more pronounced in observations than model projections, partially attributed to the Mt. Pinatubo eruption in 1991 (Arblaster et al., 2014; Remote Sensing Systems, n.d.). After a decrease of ~1 °C from 1979-1995 there has been virtually no stratospheric cooling since 1995, RSS Fig. 4. (Remote Sensing Systems, n.d.; Arblaster et al., 2014). It could even be argued that the lower stratosphere has warmed slightly since 1995.
Generally, it is expected that the stratosphere should cool as the troposphere warms, but lack of stratospheric cooling since 1996 has generated more research in this area. Stratospheric variability and stratospheric-tropospheric coupling is vital to understanding and projecting climate change. Stratospheric cooling is mainly caused by reduced ozone as a result of CFCs, and volcanic eruptions. It is also widely accepted that CO2 and other gases, including anthropogenic greenhouse gasses, cool the stratosphere but a complete understanding of the processes involved and the extent of their involvement has been elusive.
As far as surface weather measurements are concerned, certain areas of the globe known to have unreliable information such as Southeastern Asia, Siberia, areas in South America along with many ocean locations do not correspond well with satellite data while known reliable data from North America, most of Europe, and Australia does correlate well with the satellite data.
Ships have a whole different way of reporting temperature data over the oceans (SSTs) and we know the oceans cover more than 70% of the earth’s surface. The general assumption for the Sea Surface Temperature (SST) is that the surface temperature one to two meters above the sea corresponds to that of the temperature roughly one meter below the sea surface. Satellites measure at or above the surface and other data is gathered from moored and drifting buoys or other devices with sensors below the surface. The one meter depth eliminates most diurnal variation except on calm days when a diurnal thermocline may develop and persist. A diurnal thermocline is the process of heating the upper layer of water, possibly in the first several meters, into a warm stratified layer due to shortwave (solar) radiation during calm conditions resulting in weak mixing. Buoy and ship temperature measurements under these conditions are mostly unusable. Due to standard navigation lanes and known safe routes there are many areas that ships never measure and the data is affected by salt, direction of movement, wind direction, and other variables. Fixed and drifting buoys help to fill in data from some ocean areas but most of the southern hemisphere is woefully lacking in manual and instrumented records and is unmonitored not only in the oceans but also on vast areas of land. Recent ship data has been analyzed and is warmer than buoy data so additional corrections for this bias are now being considered.
Lastly, the dynamics of sea temperatures are such that the forcing processes affecting the lower atmosphere do not correlate to the processes controlling the Sea Surface Temperatures (SSTs), especially as it relates to atmospheric radiative forcing and CO2 warming. SST, the water temperature taken near the surface, is more adversely affected by wind driven upwelling or lack thereof, and locally occurring currents known as mesoscale eddies. The thermohaline circulation (THC) and multi-decadal oscillations such as the AMO, ENSO, and other climate oscillations also affect SSTs. In summary, different processes are driving the Sea Surface Temperature (SST) and Surface Air Temperature (SAT) data yet they are deemed compatible and used to determine global temperature changes despite the oceans dominating the coverage area.
Another important point regarding surface temperature measurements is the competency and commitment of those taking the observations in various economic and political environments around the globe. The surface observational data quality that is vital to climate research is not and cannot be guaranteed over some large areas as to the integrity, accuracy, and viability of that data being provided. Since the 1970’s the number of stations providing data used in surface datasets has severely declined leaving large areas of South America, Africa, and the Soviet Union sparsely represented. Some countries even consider their climate data to be proprietary.
Many adjustments are applied to surface measurements including time of observation, poor station sites, types of instrumentation used, different ways of measuring SSTs, station relocations, missing data, eliminating local station data that ‘seems’ erroneous, and many more. Finally, the surface temperature data is then amalgamated to fit a grid used by climate models. Many of these ‘adjustments’ are altering data and trends already established by respected, trusted, and accredited meteorological organizations. As one who has intimately dealt with climate data in the past, many times data quality control eliminated anomalies and interpolated and extrapolated missing data which can lead to substantial multicollinearity. Interpolation and extrapolation methods are very comprehensive and are not necessarily right or wrong they’re just not real, not verifiable, and possibly deceptive. We already have enough uncertainty in climate data; we don’t need added illusory data.
Surface observation data is collected in a myriad of ways, uses a wide array of instrumentation, is dependent on continuous calibration and maintenance procedures that differ widely, and uses numerous mathematical methods to apply corrections. Satellite measurements have unique maintenance, calibration, and diurnal and orbital correction complications and the measurements are inferred and computed through a model, however, they use the same or congruent equipment and methods to determine data values.
The satellite data, though not perfect, reduces individual station anomalies such as sensor placement, locality inconsistencies such as geographical, environmental and ecological impacts including any urban heat island effect, varying procedures used for measurement, station relocations, statistical errors in data correction, equipment differences and faults including maintenance and calibration, and large areas of land and ocean surfaces that are unobserved where data is interpolated or extrapolated with many different methods and assumptions. Radiosonde measurements of the troposphere are also suspect in some areas, especially near the tropics, but generally correlate well with satellite data trends.
In addition, many sea surface temperature datasets are using satellites to contribute as well as in situ to gather SST data while abundant research has been done with blended satellite and in situ data. Instrumentation on NASA’s Terra and Aqua satellites has provided SST data since 2000 through the Moderate Resolution Imaging Spectroradiometer (MODIS) and other instrumentation. The Advanced Very High Resolution Radiometer (AVHRR) on the NOAA Polar Orbiting Environmental Satellites (POES) contributes to measuring ocean temperature, SST, and are closely aligned and compared to buoy data gathered through the Argos Data Collection System (DCS) that can be used to calibrate algorithms for AVHRR data.
Considering the above information I find a .1-.15 °C/decade increase in a theoretical GLOBAL mean or average temperature to be quite possible but at the same time questionable as well. This does not mean that there are no other indicators that some warming has been occurring, just that both the SAT and SST data are not an ideal, comprehensive or conclusive measurement for the determination of atmospheric anthropogenic warming. In fact, all things considered the global temperature could be somewhat lower or possibly even slightly higher than measured by SAT and SST, which is why I prefer the relative objectivity of satellite measurements. Of course I realize that we don’t live at these higher altitudes and the surface temperature is more relevant to us for day to day living. Satellite data, however, samples more lower atmospheric temperature (TLT), which I consider more significant for gaining a better understanding of the comprehensive atmospheric changes occurring rather than just the surface data alone, especially when reports of multiple questionable adjustments to surface data are considered. Here is what the IPCC says about observation records:
The following is a condensed version with highlights of ‘Uncertainty in Observational Records’ from the Fifth Assessment IPCC Report.
IPCC FIFTH ASSESSMENT REPORT CLIMATE CHANGE 2013
Box 2.1: Uncertainty in Observational RecordsThe vast majority of historical (and modern) weather observations were not made explicitly for climate monitoring purposes. Measurements have changed in nature as demands on the data, observing practices and technologies have evolved. These changes almost always alter the characteristics of observational records, changing their mean, their variability or both, such that it is necessary to process the raw measurements before they can be considered useful for assessing the true climate evolution. This is true of all observing techniques that measure physical atmospheric quantities. The uncertainty in observational records encompasses instrumental / recording errors, effects of representation (e.g., exposure, observing frequency or timing), as well as effects due to physical changes in the instrumentation (such as station relocations or new satellites). All further processing steps (transmission, storage, gridding, interpolating, averaging) also have their own particular uncertainties. Since there is no unique, unambiguous, way to identify and account for non-climatic artifacts in the vast majority of records, there must be a degree of uncertainty as to how the climate system has changed…. To conclude, the vast majority of the raw observations used to monitor the state of the climate contain residual non-climatic influences. Removal of these influences cannot be done definitively and neither can the uncertainties be unambiguously assessed. (Hartmann et al., 2013, p. 165) IPCC Fifth Assessment Report, Box 2.1 | Uncertainty in Observational Records, (AR5, WG-1), Chapter 2.2, p. 165
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Weather Terms Library
General Weather Terms:
Advection: This is when heat or moisture is transferred horizontally. The atmosphere at all levels is usually in motion, with the low to middle troposphere being areas of significant advection, and thickness lines of 500-1000 hpa are often used.Airmass thunderstorm: Generally, a thunderstorm not associated with a front or other type of synoptic-scale forcing mechanism. Airmass thunderstorms typically are associated with warm, humid air in the summer months; they develop during the afternoon in response to insolation and dissipate rather quickly after sunset.Anafront: A front at which the warm air is ascending the frontal surface up to high altitudes. It tends to be active with a lot of precipitation.Anticyclogenesis: Development or intensification of a high-pressure center
Anvil: The top flatter parts of rain or storm bearing clouds.Anvil crawler: A lightning discharge occurring within the anvil of a thunderstorm, characterized by one or more channels that appear to crawl along the underside of the anvil. They typically appear during the weakening or dissipating stage of the parent thunderstorm, or during an active MCS.
Atmosphere: the air surrounding and bound to earth.
Backing or veering wind: When wind changes in an anti-clockwise direction i.e. from south to NE through east, this is said to have backed. When wind changes in a clockwise direction i.e. from southwest to north, through west, this is said to have veered.
Baroclinic zone: A region in which a temperature gradient exists on a constant pressure surface. Baroclinic zones are favored areas for strengthening and weakening systems.
Beaufort Wind Scale: A scale classifying wind strength in terms of observable effects both on the sea and over land.
Beaver(‘s) tail: A particular type of inflow band with a relatively broad, flat appearance suggestive of a beaver’s tail. It is attached to a supercell’s general updraft and is oriented roughly parallel to the pseudo-warm front, i.e., usually east to west or southeast to northwest.
Bow echo: A radar echo which is linear but bent outward in a bow shape. Damaging straight-line winds often occur near the “crest” or center of a bow echo. Areas of circulation can also develop at either end of a bow echo, which sometimes can lead to tornado genesis – especially in the right (usually southern) end, where the circulation exhibits cyclonic rotation.
CA: Cloud-to-air lightning.
CAA: Cold Air Advection
CAPE: Convective Available Potential Energy. A measure of the amount of energy available for convection. CAPE is directly related to the maximum potential vertical speed within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1,000 joules per kilogram (J/kg) and in extreme cases may exceed 5,000 J/kg.
CC: Cloud-to-cloud lightning.
CET: Abbreviation for Central European Time (= UTC +1 hour in winter, +2 hour in summer).
CG: Cloud-to-ground lightning flash.
Cloud streets: Rows of cumulus or cumulus-type clouds aligned parallel to the low-level wind flow. Cloud streets sometimes can be seen from the ground, but are seen best on satellite photographs.
Cold Pool: A region of relatively cold air, represented on a weather map analysis as a relative minimum in temperature surrounded by closed isotherms. Cold pools aloft represent regions of relatively low stability, while surface-based cold pools are regions of relatively stable air.
Comma cloud: A mesoscale cloud pattern (max 1000 km diameter) with a characteristic comma-like shape, often seen on satellite photographs associated with large, intense low-pressure systems.
Confluence: A pattern of wind flow in which air flows inward toward an axis oriented parallel to the general direction of flow. It is the opposite of difluence. Confluence is not the same as convergence. Winds often accelerate as they enter a confluent zone, resulting in speed divergence that offsets the (apparent) converging effect of the confluent flow.
Contrail: Condensation trail, a Cirrus like trail of water vapor.
Convection: Generally, transport of heat and moisture by the movement of a fluid. In meteorology, the term is used specifically to describe vertical transport of heat and moisture, especially by updrafts and downdrafts in an unstable atmosphere. Convection is not always made visible by clouds. Convection which occurs without cloud formation is called dry convection, while the visible convection processes referred to above are forms of moist. convection.
Convergence: A contraction of a vector field; the opposite of divergence. Convergence in a horizontal wind field indicates that more air is entering a given area than is leaving at that level. To compensate for the resulting “excess,” vertical motion may result: upward forcing if convergence is at low levels, or downward forcing (subsidence) if convergence is at high levels. Upward forcing from low-level convergence increases the potential for thunderstorm development (when other factors, such as instability, are favorable).
Crespuscular rays: Dark blue bands which radiate from the Sun and cross the purple light, during twilight. They are shadows of clouds situated on or below the horizon. Sometimes these shadows completely cross the sky and are again seen near the anti-solar point.
Cut-Off Low: An upper level low pressure system that is no longer in the normal west to east upper air flow. Usually a cut-off low will lie to the South of the established upper air flow.
Cyclogenesis: Development or intensification of a low-pressure center.
dBZ: The colors shown on the weather radar images represent the different echo intensities (reflectivity) measured in dBZ (decibels of Z) during each elevation scan. “Reflectivity” is the amount of transmitted power returned to the radar receiver. Reflectivity (designated by the letter Z) covers a wide range of signals (from very weak to very strong). So, a more convenient number for calculations and comparison, a decibel (or logarithmic) scale (dBZ), is used. The scale represents dBZ values of the energy reflected back to the radar from precipitation and other airborne material. The scale of dBZ values is also related to the intensity of rainfall.
Depression (Low): A mass of air with low atmospheric pressure.
Derecho: A squall line may be labeled a derecho if it is followed by an extended area of damaging winds.
Dew point (deszpoint temperature): The temperature at which condensation forms.
Deszpoint depression: The difference in degrees between the air temperature and the deszpoint temperature.
Difluence (or Diffluence): A pattern of wind flow in which air moves outward (in a “fan-out” pattern) away from a central axis that is oriented parallel to the general direction of the flow. It is the opposite of confluence.
Diurnal variation: When used in meteorology, this usually refers to the daily pattern of winds and temperatures.
Doppler radar: Radar that can measure radial velocity, the instantaneous component of motion parallel to the radar beam (i.e., toward or away from the radar antenna). Doppler radar is used by Belgocontrol (radar station at Melsbroek) for aviation safety purpose, and by KMI/IRM (radar station at Wideumont) for general forecast purpose.
Downburst : A strong downward rush of air which produces a blast of damaging wind on or close to the surface.
Downdraft: Small-scale column of air that rapidly sinks toward the ground, usually accompanied by precipitation as in a shower or thunderstorm. A downburst is the result of a strong downdraft.
Dust devils: A small but rapidly rotating column of wind of short duration that is made visible by dust, sand, and debris picked up from the ground. Diameter usually ranges from a few meter to 30 meter and develop best on clear, dry, hot afternoons.
Dust Whirl: A rotating column of air rendered visible by dust.
Entrance region: The region upstream from a wind speed maximum in a jet stream (jet max), in which air is approaching (entering) the region of maximum winds and therefore is accelerating. This acceleration results in a vertical circulation that creates divergence in the upper-level winds in the right half of the entrance region (as viewed looking along the direction of flow). This divergence results in upward motion of air in the right rear quadrant (or right entrance region) of the jet max. Severe weather potential sometimes increases in this area as a result.
Exit region: The region downstream from a wind speed maximum in a jet stream (jet max), in which air is moving away from the region of maximum winds and therefore is decelerating. This deceleration results in divergence in the upper-level winds in the left half of the exit region (as would be viewed looking along the direction of flow). This divergence results in upward motion of air in the left front quadrant (or left exit region) of the jet max. Severe weather potential sometimes increases in this area as a result.
Fog: A dense watery vapor hanging over land or sea reducing visibility less than 1 km.
Freezing level: level in atmosphere at which the temperature is 0°C.
Freezing rain: Rain that falls in liquid form and then freezes upon impact with the ground or an item with a temperature of 0 degrees Celsius or less, possibly producing a thin coating of ice. Even in small amounts, freezing rain can cause traveling problems. Large amounts can pull down power lines and tree branches. Same for Freezing Drizzle.
Front: The line that separates warm and cold fronts. Fronts are mostly accompanied by clouds usually thick enough to produce (heavy) precipitation. Behind a warm front warmer air is advected (at height), behind a cold front colder air is advected (at height). On a weather map a warm front is marked as a red line or as a black line with semicircles, a cold front as a blue line or a black line with wedges.
Frost: Crystals of frozen vapor.
Funnel cloud: A rapidly rotating column of air extending from a cumulonimbus cloud with a circulation that does not reach the ground. once a funnel cloud reaches the ground it is then called a tornado.
Gale: Wind speeds from 34 to 47 knots (61 to 85 km/h).
Geopotential: Is equivalent to the potential energy of unit mass relative to a standard level (mean sea-level by convention) and is numerically equal to the work which would be done against gravity in raising the unit mass from mean sea-level to the level at which the mass is located. When calculating the thickness of a layer, earth’s gravity force plays a role. There where gravity is largest (both poles), the layer is pulled down and becomes slightly smaller.
Geopotential height: The altitude of a layer in the atmosphere. It is expressed in geopotential meters and is equal to g/9,8 times the geopotential height expressed in (geometric) meters, g being the local acceleration of gravity. It is used to define isobaric surfaces on upper level charts. Reason for using geopotential meters instead of meters is to compare heights in geographical context, without the effect of gravity, thus imitating a perfect round earth.
Gust: A sudden increase of wind speed of short duration, usually a few seconds.
Gust front: The leading edge of the downdraft from a thunderstorm. A gust front may precede the thunderstorm by several minutes and have winds that can easily exceed 70 knots (130 km/h).
Gustnado: Gust front tornado. A small tornado, usually weak and short-lived, that occurs along the gust front of a thunderstorm. Often it is visible only as a debris cloud or dust whirl near the ground.
Hail: Frozen rain or vapor that falls in showers.
Halo: Groups of optical phenomena, in the form of rings, arcs, pillars or bright spots, produced by the refraction or reflection of light by ice crystals suspended in the atmosphere.
Hectopascal (hPa): Pressure unit much used in meteorology. A hectopascal is equal to 100 pascals or 1 millibar.
Hodograph: A plot representing the vertical distribution of horizontal winds, using polar coordinates. A hodograph is obtained by plotting the end points of the wind vectors at various altitudes, and connecting these points in order of increasing height.
Hook echo: A hook echo is displayed on radar reflectivity. It is a signature produced by precipitation held aloft that wraps around the mid-level mesocyclone. Since the mesocyclone has counterclockwise winds, the reflectivity signature of a hook echo will have a cyclonically shaped hook. The area free from reflectivity inside the hook is the updraft and inflow notch region of the supercell. A hook echo is one clue to a radar operator that a supercell has a potential of producing a tornado. Many of the violent tornadoes associated with classic supercells will show a distinct hook echo.
Humidity: The degree of moisture in the atmosphere.
Hoar frost: Deposits of ice having a crystalline appearance, generally assuming the forms of scales, needles, feathers or fans. Formed by sublimation of water vapor from surrounding clear air.
IC: Intra-cloud lightning.
Ice crystals: A barely visible crystalline form of ice that has the shape of needles, columns or plates. Ice crystals are so small that they seem to be suspended in air. Ice crystals occur at very low temperatures in a stable atmosphere.
Infra-Red: A method of viewing clouds via satellite to determine positions and condition.
Instability: The tendency for air parcels to accelerate when they are displaced from their original position; especially, the tendency to accelerate upward after being lifted. Instability is a prerequisite for severe weather – the greater the instability, the greater the potential for severe thunderstorms.
Inversion: A term meaning the reversal of something, in meteorology the a reversal of the normal atmospheric temperature gradient with height.
Irisation: Colors appearing on clouds, sometimes mingled, sometimes in the form of bands nearly parallel to the margins of the clouds. Green and pink predominate, often with pastel shades.
Isobar: A line on a weather chart showing places having the same atmospheric pressure at the same time.
Isobaric surfaces: A surface of equal pressure. Isobaric surfaces are used in upper level charts where geopotential heights are contoured to describe the upper level features. These charts are typically produced at standard levels such as 850hPa, 700hPa, 500hPa etc. In additional to geopotential height, these can charts are also used to display a variety of parameters such as streamlines, vorticity, moisture, temperature and so on. Also known as a constant pressure surface.
Jet streak: A local wind speed maximum within a jet stream.
Jet stream: A strong narrow current concentrated along a quasi horizontal axis in the upper troposphere or in the stratosphere, characterized by strong vertical and lateral wind shears and featuring one or more velocity maxima (jet streaks). The speed of the wind must be greater than 60 knots (31 m/s).
Katafront: A front (usually a cold front) at which the warm air descends the frontal surface. It tends to be “inactive” with few precipitation.
Knot: The unit of speed equal to 1.85 km/h (or 0.515 m/s or 1.152 mph). The knot is commonly used to report wind speed.
Left mover: Thunderstorm which moves to the left relative to the steering winds, and to other nearby thunderstorms; often the northern part of a splitting storm. See also right mover.
Lifted Index: (or LI) A common measure of atmospheric instability. Its value is obtained by computing the temperature that air near the ground would have if it were lifted to some higher level (usually 500mb) and comparing that temperature to the actual temperature at that level. Negative values indicate instability – the more negative, the more unstable the air is, and the stronger the updrafts are likely to be with any developing thunderstorms. However there are no “magic numbers” or threshold LI values below which severe weather becomes imminent.
Lightning: Luminous manifestation accompanying a sudden electrical discharge which takes place from or inside a cloud or, less often, from high structures on the ground or from mountains.
Macroburst: A large downburst affecting an area more than 4 kilometers (about 2.5 miles) across with damaging winds lasting from 5 to 20 minutes. May reach tornado intensity.
Maritime polar: A cold wet airmass from sub polar seas.
MCC – Mesoscale Convective Complex: A large MCS, generally round or oval-shaped, which normally reaches peak intensity at night. The formal definition includes specific minimum criteria for size, duration, and eccentricity (i.e., “roundness”), based on the cloud shield as seen on infrared satellite photographs:
Size: Area of cloud top -32 degrees C or less: 100,000 square kilometers or more, and area of cloud top -52 degrees C or less: 50,000 square kilometers or more.
Duration: Size criteria must be met for at least 6 hours.
Eccentricity: Minor/major axis at least 0.7.
MCS – Mesoscale Convective System: a complex of thunderstorms which becomes organized on a scale larger than the individual thunderstorms, and normally persists for several hours or more. MCSs may be round or linear in shape, and include systems such as tropical cyclones, squall lines, and MCCs (among others). MCS often is used to describe a cluster of thunderstorms that does not satisfy the size, shape, or duration criteria of an MCC.
Meridional flow: A type of atmospheric circulation pattern in which the north and south component of motion is unusually pronounced. Opposite of zonal flow.
Mesocyclone: a vertical column of (counterclockwise) rotating air within a severe thunderstorm which may be a precursor to a funnel or tornado. Typically a mesocyclone is 3-9 km in diameter. The circulation of a mesocyclone covers an area much larger than a tornado that may develop within it.
METAR: Meteorological Aviation Routine Weather Report, provided every half hour and consists of half-hourly observations at the airport, contains weather parameters like wind, visibility, weather, clouds, temperature, deszpoint and mean sea level pressure.
Microburst: A small, concentrated downburst affecting an area less than 4 kilometers (about 2.5 miles) across. Most microbursts are rather short-lived (5 minutes or so), but on rare occasions they have been known to last up to 6 times that long.
Mist: Suspension in the air of microscopic water droplets or wet hygroscopic particles hanging over land or sea, reducing visibility less than 5 km, but minimal 1km or more. (Criteria used in Belgium).
MSLP: Mean sea level pressure.
NOAA: National Oceanographic and Atmospheric Administration.
Nowcast: A short-term weather forecast, generally out to 6 hours or less
Occluded front-Occlusion: Fronts where the cold front has overtaken the warm front and lifted the warm air off the ground, usually meaning the end of a depression. Occluded fronts are accompanied by clouds. In the young stage they are usually thick enough to produce (heavy) precipitation. In the decaying stadium the clouds become thinner with little or no precipitation. On a weather map this front is marked as a purple line, or a black line with semicircles and wedges.
Omega blocking: A flow pattern in upper air that resembles the Greek letter Omega.
Overshooting top: A dome-like protrusion above a thunderstorm anvil, representing a very strong updraft and hence a higher potential for severe weather with that storm. A persistent and/or large overshooting top (anvil dome) often is present on a supercell.
Popcorn convection: Clouds, showers, and thundershowers that form on a scattered basis with little or no apparent organization, usually during the afternoon in response to diurnal heating.
Precipitation (ppn): Any or all of the forms of water particles, whether liquid (e.g. rain, drizzle) or solid (e.g. hail, snow), that fall from a cloud or group of clouds and reach the ground.
Precipitation intensity: divided in (s)light, moderate and heavy (or dense). Following rules are accepted depending on the type of precipitation.
Rain rate: light = maximum 0,5 mm per hour, moderate = between 0,5 and 4 mm per hour, heavy = > 4 mm per hour.
Drizzle: light = hardly any water drops are falling from objects, moderate = drops on objects fall down, dense = drops fall down and visibility is reduced remarkably.
Rain showers rate: light = maximum 2 mm per hour, moderate = between 2-10 mm per hour, heavy = > 10 mm per hour.
Snow rate: light = snow accumulation maximum 0,5 cm per hour, moderate = between 0,5 and 4 cm per hour, heavy = > 4 cm per hour.
Hail: light = few small hailstones in precipitation, moderate = surface becomes white, heavy = large hailstones > 5mm and leaves fall from trees, broken glass etc. .
When different forms of precipitation occur at same moment, the intensity is given on the precipitation type that is the most important.
Pulse-storm: strong short lived single-cell thunderstorm, which occurs with high CAPE values.
PVA: Positive Vorticity Advection. Advection of higher values of vorticity into an area, which often is associated with upward motion (lifting) of the air. PVA typically is found in advance of disturbances aloft (i.e., shortwaves) and is a property that often enhances the potential precipitation.
Radar: Radio detection and ranging is an instrument for measuring precipitation within clouds by the signal being bounced back.
Rain: The difference between rain and drizzle is that rain has a diameter greater than 0.5mm.
Rainbow: Groups of concentric arcs with colors ranging from violet to red, produced on a “screen” of water drops (raindrops, droplets of drizzle or fog) in the atmosphere by light from the Sun or Moon. This phenomena is mainly due to the refraction and reflection of light. When rainbows are produced by the Sun, their colors are usually brilliant. When produced by the Moon, their colors are much weaker or sometimes absent.
Rain foot: A horizontal bulging near the surface in a precipitation shaft, forming a foot-shaped prominence. It is a visual indication of a wet microburst.
Relative humidity: The percentage of water vapor in the air.
Ridge: An elongated area of high pressure.
Right mover: A thunderstorm that moves appreciably to the right relative to the main steering winds and to other nearby thunderstorms. Right movers typically are associated with a high potential for severe weather. (Supercells often are right movers.)
Rime: Deposits of ice, composed of grains more or less separated by trapped air, sometimes with crystalline appearance assuming the forms needles or scales. Formed by freezing of supercooled water droplets of surrounding moist air (fog or clouds).
Roll cloud: a low, horizontal tube-shaped arcus cloud associated with a thunderstorm gust front (or sometimes with a cold front); roll clouds are completely detached from the thunderstorm base or other cloud features.
Runway Visual Range (RVR): An instrumentally-derived value, based on standard calibrations, that represents the horizontal distance a pilot may see down the runway from the approach end.
SAFIR: Surveillance des orages et Alerte Foudre par Interférométrie Radioélectrique is an observation system to detect and locate lightning via VHF waves.
Scud cloud: Scattered cloud under deck cloud (fractus) = ragged detached portions of cloud under the main deck.
Secondary Cold Front: A front that follows a primary cold front and ushers in even colder air.
Severe thunderstorm: To be classified as severe, a thunderstorm has to have wind speeds of 50 knots (90 km/h) or more, and/or hail at least 2cm or more in diameter.
Shear: Wind shear is the description for when wind changes direction, usually vertical winds but not always.
Shelf cloud: A low, horizontal wedge-shaped arcus cloud, associated with a thunderstorm gust front (or occasionally with a cold front), even in the absence of thunderstorms). Unlike the roll cloud, the shelf cloud is attached to the base of the parent cloud above it (usually a thunderstorm). Rising cloud motion often can be seen in the leading (outer) part of the shelf cloud, while the underside often appears turbulent, boiling, and wind-torn.
Shower: Intermittent and usually short spells of precipitation.
Single-cell: One thunderstorm cell. It has a duration of around half an hour.
Sleet: Wintry precipitation
mix of rain and snow (used in the U.K.)
mix of rain and hail
frozen raindrops that bound on impact with the ground (used in the U.S.A.).
Snow: Intricately shaped ice crystals that fall as precipitation.
Snowflake: White ice crystals that have combined in a complex branched hexagonal form.
Sounding: a plot of the vertical profile of temperature and dew point (and often winds) above a fixed location; used extensively in weather forecasting.
Splitting storms: A thunderstorm which splits into two storms which follow diverging paths (a left mover and a right mover). The left mover typically moves faster than the original storm, the right mover, slower. Of the two, the left mover is most likely to weaken and dissipate (but on rare occasions can become a very severe anticyclonic-rotating storm), while the right mover is the one most likely to reach supercell status.
Squall: Atmospheric phenomenon characterized by a very large variation of wind speed, often accompanied by a shower or thunderstorm.
Squall Line: Fictitious moving line, sometimes of considerable extent, along which squall phenomena occur.
Stable Air: Air that is colder than its surroundings and as such is resistant to upward movement.
Storm: a disturbance of the atmosphere marked by wind and usually by rain, snow, hail, sleet or thunder and lightning.
Subsidence: Sinking (downward) motion in the atmosphere, usually over a broad area.
Sublimation: The transition of a substance from the solid phase directly to the vapour phase, or vice versa, without passing through the liquid phase.
Supercell (Supercell storm): A violent thunderstorm which last for several hours, sometimes causing torrential rain and tornadoes.
TAF: Terminal Aerodrome Forecast contains a weather forecast for a specific airport for the oncoming day(s).
Tail cloud: A low tail-shaped cloud extending outward from the northern quadrant of a wall cloud. Motions in the tail cloud are toward the wall cloud with rapid updraft at the junction of tail and wall cloud. This horizontal cloud is not a funnel or tornado.
Tail-end Charlie: The thunderstorm found at the most southern end of a squall line or band of thunderstorms. Since low-level southerly inflow of warm, moist air into this storm is relatively unimpeded, such a storm often has a higher probability of strengthening to severe levels than the other storms in the line.
Temperature: The degree of hotness or coldness as measured on some definite temperature scale.
Thickness: Thickness usually refers to the depth of the 1000-500 hPa layer in the atmosphere. However charts are also produced for thicknesses of other layers in the atmosphere as well. The thickness gives an indication of the mean temperature within a layer; lower thicknesses indicate colder air, higher thicknesses warmer air.
Thunder: The sound caused by air expanding, following a flash of lightning.
Thunderstorm: A local storm produced by a cumulonimbus cloud, always with lightning and thunder, and usually accompanied by strong gusts of wind, heavy rain, and sometimes hail.
Tornado: A rapidly rotating column of air extending from a cumulonimbus cloud with a circulation that reaches the ground. However, the visible portion might not extend all the way to the ground.
Towering Cumulus: A large Cumulus cloud with great vertical development, usually with a cauliflower-like appearance, but lacking the characteristic anvil of a Cb. (Often shortened to “towering cu,” and abbreviated TCU)
TREND: gives very accurate meteorological information at a specific airport and is indispensable for landing of aircraft within the next 2 hours. TREND is part of METAR.
Triple point: The intersection point between boundaries of different air masses (warm front, cold front, occlusion, etc) often a focus for severe weather development.
Tropopause: The place that marks the end of the troposphere and the start of the stratosphere marked by a change in temperature.
Trough: An elongated area of relatively low atmospheric pressure, usually not associated with a closed circulation, and thus used to distinguish from a closed low.
Turbulence: Disturbance in the atmosphere causing gusts of varying strengths.
Unstable air: Air that is warmer than its surroundings and as such tends to rise, leading to the formation of clouds and possibly precipitation.
Updraft: A small-scale current of rising air. If the air is sufficiently moist, then the moisture condenses to become a Cumulus cloud or an individual tower of a towering cumulus or Cb.
UTC: Abbreviation for Universal Time Coordinated and formerly known as Greenwich Mean Time (GMT).
Visibility: Greatest distance at which a black object of suitable dimensions can be seen and recognized against the horizon sky, or, in the case of night observations, could be seen and recognized if the general illumination were raised to the normal daylight level.
Vortex: Cyclonic flow in a relative small area.
WAA: Warm Air Advection.
Wall cloud: Lowering of a cloud base at low levels in a thunderstorm.
Waterspout: A rapidly rotating column of air extending from a cumulonimbus cloud with a circulation that reaches the surface of the water, (i.e. a tornado over water).
Wind: a natural movement of air at a velocity relative to the surface of earth.
Wind shear: the local variation of the wind speed and/or direction in a given direction. Shear usually refers to vertical wind shear (i.e., the change of wind with height) but the term is also used in Doppler radar meteorology to describe changes in radial velocity over short horizontal distances.
Wind shift: A sudden change of wind direction.
Zonal flow: Large-scale atmospheric flow in which the east-west (latitudinal) component is dominant.
Zulu time: Same as UTC, Universal Coordinated Time. It is called Zulu because “Z” is often appended to the time to distinguish it from local time.
Source: WMO publication No. 182. TP. 91
Other Sources of Weather Terms:
NOAA National Weather Service Glossary: https://forecast.weather.gov/glossary.php
American Meteorological Society (AMS) Glossary of Meteorology:http://glossary.ametsoc.org/wiki/Main_Page https://www.islandnet.com/~see/weather/general/wxgls_ad.htm http://www.stormfax.com/wxwords.htm https://kestrelmeters.com/pages/weather-glossary
Other terms (quick reference):
Outflow Boundary – A storm-scale or mesoscale boundary separating thunderstorm-cooled air (outflow) from the surrounding air; similar in effect to a cold front, with passage marked by a wind shift and usually a drop in temperature. Outflow boundaries may persist for 24 hours or more after the thunderstorms that generated them dissipate, and may travel hundreds of miles from their area of origin. New thunderstorms often develop along outflow boundaries, especially near the point of intersection with another boundary (cold front, dry line, another outflow boundary, etc.; see triple point).
Conditional Symmetric Instability (CSI):https://www.weather.gov/media/lmk/soo/CSI_EPV_Web.pdf https://journals.ametsoc.org/view/journals/wefo/13/1/1520-0434_1998_013_0084_eaaocs_2_0_co_2.xml?rskey=eYdlyK&result=9 http://www.storm2k.org/phpbb2/viewtopic.php?t=19472
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Which models suck and why?
Numerical weather prediction is extremely complex! Here is a simplified list of all of the aspects of NWP forecasts:
-mathematical formulation of the PDEs that govern the atmosphere (typically called “model dynamics”)
-treatment of sub-grid scale processes (depends on the model resolution, typically called “model physics” or “parameterizations”)
-initial and lateral boundary condition data
-model configuration (horizontal and vertical resolution, finite difference or spectral, time step, vertical coordinate, number of soil levels or ocean levels, topography)
Things you need to understand about each model to really get an idea how it should differ from other models
-Which schemes are used to discretize the equations? Leapfrog? Adams-Bashforth? Forward Euler? Backward Euler? Each one has known strengths and weaknesses.
-What order of truncation was used for each scheme? Higher order schemes generally give better results, but also increase computational expense.
-Is this model using finite differencing to represent the derivatives or is it using Fourier series and waves to represent the fields?
-Which sub-grid scale processes are being parameterized? Deep convection? Shallow convection? cloud/rain physics? boundary layer? land surface? urban surface? sub-surface? radiation?
-For each process that is being parameterized, which scheme is being used? For example, there are about 3 or 4 different cumulus parameterization schemes that operational forecast models use. Some are well documented and their strengths and weaknesses well known, while others are new or are improved versions of well known schemes but haven’t been rigorously verified or documented. For some schemes, no documentation exists at all (it was written and maintained by one person). Keep in mind that although Weisman et al. (1997) is typically cited as the paper that said you don’t need to use convective parameterization starting at 4 km grid spacing, but convective processes are not resolved at 4 km! The entire range between about 1 km and 10 km is a gray zone where conventional convective parameterization schemes used in many modern forecast models are not meant to be used, but deep convection is still not fully resolved. It’s inaccurate and unfair to call 4 km models “convection-resolving”, because they aren’t.
Initial and lateral boundary condition data
-This is where the meat of the PDF that Rob linked to falls. The amount, type, and quality of data ingested and processed by data assimilation schemes must be known. Also, there are different types of data assimilation (3DVAR, 4DVAR, EnKF etc.), and different configurations within each type of assimilation. There are also different ways of taking irregularly spaced data and transforming it to a gridded array (Cressman, Barnes etc.). Many of these are well documented and have known strengths and weaknesses (advantages/disadvantages), but you need to know which model system uses what.
-Global models don’t need lateral boundary condition data, but “limited area” models like the NAM, RAP, HRRR, SREF etc. do. Limited area model output is strongly correlated with the skill of the model that provided the lateral boundary conditions past a certain forecast hour (depending on the size of the limited area model domain). Also, how was the lateral boundary condition data used? Was it only applied to the outermost grid point? The outer 5? Was it filtered at all?
-Horizontal resolution is big, obviously. But one thing many people tend to overlook is the vertical resolution. Back in the day when grid spacings were tens of kilometers, grid columns were wide and short, as the vertical resolution was much finer than the horizontal resolution. Vertical resolution hasn’t increased nearly as much as horizontal resolution has. In convection-allowing models today, the grid columns are much skinnier than they used to be, as individual grid boxes are much taller than they used to be. This impacts how processes such as convection are treated.
-Vertical coordinate: while the model output you see on websites is generally given on isobaric surfaces, NWP models generally do not use an isobaric or fixed height vertical coordinate. Most models use a terrain following sigma or eta vertical coordinate, or an isentropic one (the RUC used a hybrid isentropic-sigma coordinate).
-Topography: when you setup a WRF run, you can select the quality of the topography that the model assumes. This is hugely significant when considering processes impacted by interaction with the Earth’s surface.
-Is the model strictly an atmospheric model (having only grid points within the atmosphere)? Many climate models are actually “Earth system” models that include grid points in the soil and under water, and include dynamics and physics parameterizations to prognosticate soil temperature, soil moisture, SST etc.
-As mentioned before, the output you see on a website is not the raw model output. Rather, the output was post-processed from the native model levels to isobaric or iso-height surfaces. There are different ways to interpolate vertically.
-Was there a post-processing scheme or method used to alter the raw model output to either correct for known biases in the model or to force ensemble output to fit a Gaussian distribution? This is especially important when viewing output from ensembles. Also keep in mind that while you can find “CAPE” as a field to view in model output, you should determine if it’s surface-based, mixed-layer, most-unstable, or some other level CAPE. Some websites don’t distinguish between those types. Also, did they use the virtual temperature correction? The GFS didn’t until a few years ago. Not sure about anything else.
As MClarkson said, you can compile your own error statistics by obtaining a large sample size to determine any deficiencies or particular strengths of a model. However, to really know, you’ll need to know every aspect of the model to be sure. Also, keep in mind that error statistics are heavily quantitative, yet Rob asked questions like “GFS always has systems that are too fast/slow”, which is much more qualitative, and isn’t easily addressed by examining basic error statistics. This is because you are crossing the line between pure quantitative statistics and into feature-based identification, which computers are much farther behind compared to how they do with pure quantitative statistics
Many thanks to Jeff Dudafor that extremely detailed and helpful information on climate models! -Mike