scholarly journals Atmospheric Precipitable Water and its Correlation with Clear Sky Infrared Temperature Observations

2021 ◽  
Author(s):  
Vicki Kelsey ◽  
Spencer Riley ◽  
Kenneth Minschwaner
Keyword(s):  
New Mexico ◽  
Weather Forecasting ◽  
Gulf Coast ◽  
Precipitable Water ◽  
Exponential Relationship ◽  
Annual Cycles ◽  
Positioning Systems ◽  
Desert Southwest ◽  
Forecast Models ◽  
Microwave Radiometers

Abstract. Total precipitable water (TPW) in the atmosphere is the vertically integrated amount of atmospheric water in all of its phases. TPW is a valuable predictor for weather forecasting, and it is routinely measured using radiosondes, ground-based global positioning systems (GPS), sun photometers, or microwave radiometers. The use of these sophisticated instruments limits the number of TPW measurement sites, which affects the accuracy of forecast models in regards to storm formation, strength, and the potential for precipitation. We have analyzed this relationship for the much drier climate zone found in the Desert Southwest, specifically over Socorro, New Mexico (34° N, 107° W). Daily measurements of the ground and zenith sky temperatures have been made at Socorro for two complete annual cycles using infrared thermal sensors. Radiosonde TPW measurements from National Weather Service stations located in nearby Albuquerque, and Santa Theresa, New Mexico, are input into our dataset and analysed via a newly developed computational tool. Our results show that an exponential relationship between TPW and zenith sky temperature also holds for the Desert Southwest, but with parameters that are different than those obtained for the Gulf Coast. Model simulations can accurately reproduce the observed relationship between TPW and temperature, and the results suggest that half of the signal in temperature is directly related to direct changes in opacity due to changes in TPW, while the other half is due to changes in air temperature that usually accompany changes in TPW.

scholarly journals How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

2019 ◽  
Vol 100 (4) ◽  
pp. 605-619 ◽  
Author(s):  
A. J. Illingworth ◽  
D. Cimini ◽  
A. Haefele ◽  
M. Haeffelin ◽  
M. Hervo ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Positive Impact ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Time Data ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Wind Lidar ◽  
Microwave Radiometers

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

scholarly journals Assessments of GMI-Derived Precipitable Water Vapor Products over the South and East China Seas Using Radiosonde and GNSS

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Biyan Chen ◽  
Wujiao Dai ◽  
Zhizhao Liu ◽  
Lixin Wu ◽  
Pengfei Xia
Keyword(s):  
Water Vapor ◽  
Weather Forecasting ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Weather Conditions ◽  
Precipitable Water Vapor ◽  
East China ◽  
China Seas ◽  
The South ◽  
Mean Square Errors

Satellite remote sensing of the atmospheric water vapor distribution over the oceans is essential for both weather and climate studies. Satellite onboard microwave radiometer is capable of measuring the water vapor over the oceans under all weather conditions. This study assessed the accuracies of precipitable water vapor (PWV) products over the south and east China seas derived from the Global Precipitation Measurement Microwave Imager (GMI), using radiosonde and GNSS (Global Navigation Satellite System) located at islands and coasts as truth. PWV measurements from 14 radiosonde and 5 GNSS stations over the period of 2014–2017 were included in the assessments. Results show that the GMI 3-day composites have an accuracy of better than 5 mm. A further evaluation shows that RMS (root mean square) errors of the GMI 3-day composites vary greatly in the range of 3∼14 mm at different radiosonde/GNSS sites. GMI 3-day composites show very good agreements with radiosonde and GNSS measured PWVs with correlation coefficients of 0.896 and 0.970, respectively. The application of GMI products demonstrates that it is possible to reveal the weather front, moisture advection, transportation, and convergence during the Meiyu rainfall. This work indicates that the GMI PWV products can contribute to various studies such as climate change, hydrologic cycle, and weather forecasting.

A long-term perspective on woody plant encroachment in the desert southwest, New Mexico, USA

2013 ◽  
Vol 25 (3) ◽  
pp. 829-838 ◽  
Author(s):  
A. Brunelle ◽  
T.A. Minckley ◽  
J. Delgadillo ◽  
S. Blissett
Keyword(s):  
New Mexico ◽  
Woody Plant ◽  
Woody Plant Encroachment ◽  
Desert Southwest

Examination of Convection-Allowing Configurations of the WRF Model for the Prediction of Severe Convective Weather: The SPC/NSSL Spring Program 2004

2006 ◽  
Vol 21 (2) ◽  
pp. 167-181 ◽  
Author(s):  
John S. Kain ◽  
S. J. Weiss ◽  
J. J. Levit ◽  
M. E. Baldwin ◽  
D. R. Bright
Keyword(s):  
Weather Forecasting ◽  
Wrf Model ◽  
Value Added ◽  
Severe Weather ◽  
Convective Initiation ◽  
A Value ◽  
Human Decision ◽  
Forecast Models ◽  
Convective Weather ◽  
The Impact

Abstract Convection-allowing configurations of the Weather Research and Forecast (WRF) model were evaluated during the 2004 Storm Prediction Center–National Severe Storms Laboratory Spring Program in a simulated severe weather forecasting environment. The utility of the WRF forecasts was assessed in two different ways. First, WRF output was used in the preparation of daily experimental human forecasts for severe weather. These forecasts were compared with corresponding predictions made without access to WRF data to provide a measure of the impact of the experimental data on the human decision-making process. Second, WRF output was compared directly with output from current operational forecast models. Results indicate that human forecasts showed a small, but measurable, improvement when forecasters had access to the high-resolution WRF output and, in the mean, the WRF output received higher ratings than the operational Eta Model on subjective performance measures related to convective initiation, evolution, and mode. The results suggest that convection-allowing models have the potential to provide a value-added benefit to the traditional guidance package used by severe weather forecasters.

scholarly journals A Comparative Study of Weather Forecasting Simulation Models through Sensors for Accurate Monsoon Predictions in the Indian Ocean

2020 ◽  
Vol 10 (1) ◽  
pp. 331-336
Keyword(s):  
Comparative Study ◽  
Weather Forecasting ◽  
Precipitable Water ◽  
Horizontal Resolution ◽  
Horizontal Wind ◽  
Equitable Threat Score ◽  
Horizontal Wind Speed ◽  
Bias Score ◽  
Threat Score ◽  
Humidity Profiles

When Monsoon depressions form over the seas, the Moderate Resolution Imaging Spectroradiometer (MODIS) provides humidity and high-horizontal resolution temperature details about the depressions. These high-resolution satellite data related to temperature and humidity can improve the poor predicting rate of depressions [1]. Using three-dimensional variational data assimilation (3DVAR) and with the help of humidity profiles along with MODIS temperature. We can achieve an advanced prospect of detection and a larger value of (ETS) equitable threat score observed over 48 hours collected precipitation with respect to the control run. The 3DVAR assimilation of Doppler Weather Radar wind data associated with Indian Meteorological Department (IMD) surface data and upper air data helped in the improvements in the simulation of strong gradients associated with horizontal wind speed ,higher warm core temperature , high vertical velocity & better precipitation and spatial distribution.[2]. The effect of Spectral sensor microwave imager (SSM/I), humidity profiles, use of Advanced TIROS Vertical Sounder (ATOVS) temperature and total precipitable water (TPW) helped in improving the ‘‘forecast impact’’ parameters of ‘‘bias score’’ and ‘‘equitable threat score’’ with respect to the assimilation of satellite observation[3] . In this paper we have discussed a comparative study of different proposed techniques to analyze its effects in improving the low prediction rates of depressions.

scholarly journals Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2015 ◽  
Vol 8 (10) ◽  
pp. 10755-10792
Author(s):  
A. M. Dzambo ◽  
D. D. Turner ◽  
E. J. Mlawer
Keyword(s):  
Water Vapor ◽  
Statistical Significance ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Upper Troposphere ◽  
Clear Sky ◽  
Microwave Radiometers ◽  
The Impact ◽  
Dry Bias ◽  
Better Than

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km MSL), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS). The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from ground-based microwave radiometers at tropical, mid-latitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected RH are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover – a condition not accounted for in the radiance closure experiments.

scholarly journals A 30-year climatology (1990-2020) of aerosol optical depth and total column water vapor and ozone over Texas

2021 ◽  
pp. 1-22
Author(s):  
Forrest M. Mims
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Precipitable Water ◽  
Texas History ◽  
Total Column Ozone ◽  
Annual Cycles ◽  
Total Column

AbstractA 30-year time series (4 Feb 1990 to 4 Feb 2020) of aerosol optical depth of the atmosphere (AOD), total precipitable water (TPW) and total column ozone has been conducted in Central Texas using simple, highly stable instruments. All three parameters in this ongoing measurement series exhibited robust annual cycles. They also responded to many atmospheric events, including the historic volcanic eruption of Mount Pinatubo (1991), a record El Niño (1998), an unprecedented biomass smoke event (1998) and the La Niña that caused the driest drought in recorded Texas history (2011). Reduced air pollution caused mean AOD to decline from 0.175 to 0.14. The AOD trend measured for 30 years by an LED sun photometer, the first of its kind, parallels the trend from 20 years of measurements by a modified Microtops II. While TPW responded to El Niño-Southern Oscillation conditions, TPW exhibited no trend over the 30 years. The TPW data compare favorably with 4.5 years of simultaneous measurements by a nearby NOAA GPS (r2 = 0.78). The 30 years of ozone measurements compare favorably with those from a series of NASA ozone satellites (r2 = 0.78). In 2016, 194 comparisons of Microtops II and world standard ozone instrument Dobson 83 at the Mauna Loa Observatory agreed within 1.9% (r2 = 0.81). The paper concludes by observing that students and citizen scientists can collect scientifically useful atmospheric data with simple sun photometers that use one or more LEDs as spectrally selective photodiodes.

scholarly journals Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2016 ◽  
Vol 9 (4) ◽  
pp. 1613-1626 ◽  
Author(s):  
Andrew M. Dzambo ◽  
David D. Turner ◽  
Eli J. Mlawer
Keyword(s):  
Water Vapor ◽  
Statistical Significance ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Upper Troposphere ◽  
Clear Sky ◽  
Microwave Radiometers ◽  
The Impact ◽  
Dry Bias ◽  
Better Than

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS). The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from ground-based microwave radiometers at tropical, midlatitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected RHs are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover – a condition not accounted for in the radiance closure experiments.

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