Climatology

During the early 20th century, climatology was mostly emphasized the description of regional climates. This descriptive climatology was mainly an applied science, giving farmers and other interested people statistics about what the normal weather was and how great chances were of extreme events.[8] To do this, climatologists had to define a climate normal, or an average of weather and weather extremes over a period of typically 30 years.[9] While scientists knew of past climate change such as the ice ages, the concept of climate as changing only very gradually was useful for descriptive climatology. This started to change during the decades that followed, and while the history of climate change science started earlier, climate change only became one of the main topics of study for climatologists during the 1970s and afterward.[10]

Various subtopics of climatology study different aspects of climate. There are different categorizations of the sub-topics of climatology. The American Meteorological Society for instance identifies descriptive climatology, scientific climatology and applied climatology as the three subcategories of climatology, a categorization based on the complexity and the purpose of the research.[11] Applied climatologists apply their expertise to different industries such as manufacturing and agriculture.[12]

Climatology

DOWNLOAD 🔥 https://blltly.com/2y2EXe 🔥

Paleoclimatology is the attempt to reconstruct and understand past climates by examining records such as ice cores and tree rings (dendroclimatology). Paleotempestology uses these same records to help determine hurricane frequency over millennia. Historical climatology is the study of climate as related to human history and is thus concerned mainly with the last few thousand years.

Boundary-layer climatology concerns exchanges in water, energy and momentum near surfaces.[13] Further identified subtopics are physical climatology, dynamic climatology, tornado climatology, regional climatology, bioclimatology, and synoptic climatology. The study of the hydrological cycle over long time scales is sometimes termed hydroclimatology, in particular when studying the effects of climate change on the water cycle.[11]

Climatology is the study of the atmosphere and weather patterns over time. This field of science focuses on recording and analyzing weather patterns throughout the world and understanding the atmospheric conditions that cause them. It is sometimes confused with meteorology, which is the study of weather and weather forecasting. However, climatology is mainly focused on the natural and artificial forces that influence long-term weather patterns. Scientists who specialize in this field are called climatologists.

Students enrolled in the Climatology minor learn a wide range of research and analytical skills that are highly valued by employers. Students trained in climatology find jobs in all levels of government, nonprofit organizations, and in industry.

Staff used a variety of tests to assess data quality. The first step involved comparing stations with a gridded climatology and plotting the stations for visual inspection. Both of these processes uncovered mislocated stations and the digitized formerly uncovered stations 6 months out of phase. Additionally, each time series was tested for significant discontinuities using the Cumulative Sum test (which looks for changes in the mean) and an analogous test that looks for changes in the variance or scale. Evaluation of each time series for runs of three or more months of the same nonzero value. Finally, scientists evaluated each individual precipitation total to determine if it was an outlier in space and/or time using a variety of nonparametric statistics.

The Journal of Applied Meteorology and Climatology (JAMC) (ISSN: 1558-8424; eISSN: 1558-8432) publishes applied research on meteorology and climatology. Examples of meteorological research include topics such as weather modification, satellite meteorology, radar meteorology, boundary layer processes, physical meteorology, air pollution meteorology (including dispersion and chemical processes), agricultural and forest meteorology, mountain meteorology, and applied meteorological numerical models. Examples of climatological research include the use of climate information in impact assessments, dynamical and statistical downscaling, seasonal climate forecast applications and verification, climate risk and vulnerability, development of climate monitoring tools, and urban and local climates.

A monthly climatology, for example, will produce a mean value for each month and a daily climatology will produce a mean value for each day, over a specified time range. Anomalies, or the deviation from the mean, are created by subtracting climatological values from observed data.

Example: Generate a composite of Oct-Dec seasonal average monthly zonal surface wind anomalies from the 1951-2006 climatology for Oct-Dec El Nio seasons (based upon the Climate Prediction Center definition) during that range of years.

Example pages containing:tips |resources | functions/procedures

NCL: ClimatologyThere are numerous climatological functions that compute daily and monthly climatologies; calculate anomalies from the climatologies;remove monthly and daily annual cycles, and, calculate interannual variabilities.For historical reasons, some of these function names end inTLL, TLLL, LLT, LLLT. They referto expected input array ordering (nominally): (time,lat,lon), (time,lev,lat,lon),(lat,lon,time), (lev,lat,lon,time), respectively.This page illustrates some simple applications of these functions.climo_0.ncl: Compute monthly climatologiesand the monthly interannual variabilities using contributed functions clmMonTLL and clmStdTLL.Built-in functions used: cd_calendar, indOnly the January and July climaytologies are displayed.climo_1.ncl: Compute decadal meansand standard deviation for SLP for two different decades, compute thet-statistic, and plot the 5% level as stippling.Built-in functions used: ttest, ind.Contributed functions used:clmMonTLL,stdMonTLL,copy_VarCoords.runave_n_Wrap.Shea_util functions used:ShadeLtContour.climo_2.ncl: Calculates monthlyclimatologies and then conducts an eof analysis.Built-in functions used: runave, dimsizes. Contributed functions used:clmMonLLT,stdMonLLT,eofcov_ts_Wrap.climo_3.ncl: Demonstrates the use ofclmMonLLT and stdMonTLL to derive climatology and theinterannual variability. Though this example derives the climatologybased on the entire time period, a subset may be used by using eitherconventional subscripting or coordinate dimensions.To get the climatology for Jan 1980 through Dec 1989 for this dataset:prcClm = clmMonTLL(prc(12:131,:,:)), using conventional subscripts.or prcClm = clmMonTLL(prc({198001:198912},:,:)), using coordinate subscripting. climo_4.ncl: Demonstrates the use ofclmMonLLT to derive a zonallyaveraged annual cycle.climo_5.ncl: Calculate the daily mean annual cycle and daily anomalies from the meanannual cycle. For illustration: (a) compute raw and smoothed annual cycles; (b) create a netCDF file of the daily anomalies; (c) plot results.This example only uses 5-years of data. Hence, there is considerableday-to-day variability in this example. Which is the proper daily annual cycle to use: raw or smoothed? It depends on your usage. The smoothed annual cyclecan be thought of as the values that would be obtainedif there was an infinite ensemble of data under the same forcing conditions.climo_6.ncl: (a) Read files containing year-month data, (b) create climatologies spanning user specified years (c) plot November-April and May-October climatologies over a user specified region

Abstract. Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05) global precipitation climatologies that perform reasonably well in data-sparse regions.

Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05 monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, doi:10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities. ff782bc1db