scholarly journals An Integrated Assessment of Measured and Modeled Integrated Water Vapor in Switzerland for the Period 2001–03

2005 ◽  
Vol 44 (7) ◽  
pp. 1033-1044 ◽  
Author(s):  
G. Guerova ◽  
E. Brockmann ◽  
F. Schubiger ◽  
J. Morland ◽  
C. Mätzler
Keyword(s):  
Water Vapor ◽  
Standard Deviation ◽  
Diurnal Cycle ◽  
Integrated Assessment ◽  
Mesoscale Model ◽  
Microwave Radiometer ◽  
Measurement Techniques ◽  
Bimodal Distribution ◽  
Limited Area ◽  
Dry Bias

Abstract In this paper an integrated assessment of the vertically integrated water vapor (IWV) measured by radiosonde, microwave radiometer (MWR), and GPS and modeled by the limited-area mesoscale model of MeteoSwiss is presented. The different IWV measurement techniques are evaluated through intercomparisons of GPS to radiosonde in Payerne, Switzerland, and to the MWR operated at the Institute of Applied Physics at the University of Bern in Switzerland. The validation of the IWV field of the nonhydrostatic mesoscale Alpine Model (aLMo) of MeteoSwiss is performed against 14 GPS sites from the Automated GPS Network of Switzerland (AGNES) in the period of 2001–03. The model forecast and the nudging analysis are evaluated, with special attention paid to the diurnal cycle. The results from the GPS–radiosonde intercomparison are in agreement, but with a bimodal distribution of the day-to-night basis. At 0000 UTC, the bias is negative (−0.4 kg m−2); at 1200 UTC, it is positive (0.9 kg m−2) and the variability increases. The intercomparison of GPS to MWR shows better agreement (0.4 kg m−2), with a small increase of the daytime bias with 0.3 kg m−2. The intercomparison of MWR to the radiosonde gives a bimodal distribution of the bias, with an increase in the standard deviation at the daytime measurement. The relative bias is negative (−3%) at 0000 UTC and is positive (3%) at 1200 UTC. Based on this cross correlation, it can be concluded that the bimodal distribution is a result of radiosonde humidity measurements. Possible reasons are the solar-heating correction or sensor errors. The monthly bias and standard deviation of aLMo exhibit a strong seasonal dependence with a pronounced dry bias during the warm months of May–October 2002. The diurnal IWV cycle in 2001 shows good model performance between 0000 and 0900 UTC but IWV underestimation by up to 1.5 kg m−2 for the rest of the day. In 2002 the diurnal cycle shows a systematic dry bias in both the analysis and forecast that is more pronounced in the analysis. This substantial underestimation of IWV was found to correlate with overestimation of aLMo precipitation, especially light precipitation up to 0.1 mm (6 h)−1 in 2002. There is strong evidence that this underestimation can be related to the dry radiosonde bias in midday summer observations. The aLMo dry bias is about 1.0–1.5 kg m−2 greater in the nudging analysis as compared with the forecast initialized at 0000 UTC.

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 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.

Improved Daytime Column-Integrated Precipitable Water Vapor from Vaisala Radiosonde Humidity Sensors

2008 ◽  
Vol 25 (6) ◽  
pp. 873-883 ◽  
Author(s):  
K. E. Cady-Pereira ◽  
M. W. Shephard ◽  
D. D. Turner ◽  
E. J. Mlawer ◽  
S. A. Clough ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Transmittance ◽  
Dry Bias

Abstract Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

A Comparison of GOES Moisture-Derived Product and GPS-IPW Data during IHOP-2002

2005 ◽  
Vol 22 (11) ◽  
pp. 1838-1845 ◽  
Author(s):  
Daniel Birkenheuer ◽  
Seth Gutman
Keyword(s):  
Water Vapor ◽  
Standard Deviation ◽  
Field Experiment ◽  
Diurnal Cycle ◽  
Precipitable Water ◽  
Data Sources ◽  
Signal Delay ◽  
Total Precipitable Water ◽  
Limited Degree ◽  
Total Column

Abstract Geostationary Operational Environmental Satellite (GOES) sounder–derived total column water vapor is compared with other data sources obtained during the 2002 International H2O Project (IHOP-2002) field experiment. Specifically, GPS-derived total integrated precipitable water (GPS-IPW) and radiosonde observations (raob) data are used to assess GOES bias and standard deviation. GPS integrated water calculated from signal delay closely matches raob data, both from special sondes launched for the IHOP-2002 exercise and routine National Weather Service (NWS) soundings. After examining the average differences between GPS and GOES product total precipitable water over the full diurnal cycle between 26 May and 15 June 2002, it was discovered that only 0000 UTC time differences were comparable to published comparisons. Differences at other times were larger and varied by a factor of 6, increasing from 0000 to 1800 UTC, and decreasing thereafter. Reasons for this behavior are explored to a limited degree but with no clear answers to explain the observations. It is concluded that a component of the GOES total precipitable water error (between sonde launches) might be missed when solely assessing the data against synoptic raobs.

scholarly journals Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

2017 ◽  
Author(s):  
Maria Filioglou ◽  
Anna Nikandrova ◽  
Sami Niemelä ◽  
Holger Baars ◽  
Tero Mielonen ◽  
...  
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Limited Area ◽  
Raman Lidar ◽  
Model Data ◽  
Mixing Ratios ◽  
Field Campaigns ◽  
Satellite Product

Abstract. We present tropospheric water vapor profiles measured with a Raman lidar during three field campaigns held in Finland. Co-located radio soundings are available throughout the period for the calibration of the lidar signals. We investigate the possibility of calibrating the lidar water vapor profiles in the absence of co-existing on site soundings using water vapor profiles from the combined Advanced Infrared Sounder (AIRS) and the Advanced Microwave Radiometer (AMSU) satellite product; the Aire Limitee Adaptation dynamique Development International and High Resolution Limited Area Model (ALADIN HIRLAM) numerical weather prediction (NWP) system, and the nearest radio sounding station located 100 km away from the lidar site (only for the permanent location of the lidar). The uncertainties of the calibration factor derived from the soundings, the satellite and the model data are

scholarly journals New Gridded Product for the Total Columnar Atmospheric Water Vapor over Ocean Surface Constructed from Microwave Radiometer Satellite Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2402
Author(s):  
Weifu Sun ◽  
Jin Wang ◽  
Yuheng Li ◽  
Junmin Meng ◽  
Yujia Zhao ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Atmospheric Water Vapor ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Fusion Product ◽  
Mean Deviation ◽  
Atmospheric Water ◽  
Absolute Deviation ◽  
Better Than

Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radiometer (MR). The accuracy of this water vapor fusion product was validated using radiosonde water vapor observations. The comparative results show that the overall mean deviation (Bias) is smaller than 0.6 mm; the root mean square error (RMSE) and standard deviation (SD) are better than 3 mm, and the mean absolute deviation (MAD) and correlation coefficient (R) are better than 2 mm and 0.98, respectively.

scholarly journals Water vapor transport in the lower mesosphere of the subtropics: a trajectory analysis

2008 ◽  
Vol 8 (23) ◽  
pp. 7273-7280 ◽  
Author(s):  
T. Flury ◽  
S. C. Müller ◽  
K. Hocke ◽  
N. Kämpfer
Keyword(s):  
Water Vapor ◽  
Trajectory Analysis ◽  
Microwave Radiometer ◽  
Rotational Transition ◽  
Transport Processes ◽  
Water Vapor Transport ◽  
Transition Line ◽  
Wind Fields ◽  
Large Variability ◽  
Northern India

Abstract. The Institute of Applied Physics operates an airborne microwave radiometer AMSOS that measures the rotational transition line of water vapor at 183.3 GHz. Water vapor profiles are retrieved for the altitude range from 15 to 75 km along the flight track. We report on a water vapor enhancement in the lower mesosphere above India and the Arabian Sea. The measurements took place on our flight from Switzerland to Australia and back in November 2005 conducted during EC- project SCOUT-O3. We find an enhancement of up to 25% in the lower mesospheric H2O volume mixing ratio measured on the return flight one week after the outward flight. The origin of the air is traced back by means of a trajectory model in the lower mesosphere and wind fields from ECMWF. During the outward flight the air came from the Atlantic Ocean around 25 N and 40 W. On the return flight the air came from northern India and Nepal around 25 N and 90 E. Mesospheric H2O measurements from Aura/MLS confirm the transport processes of H2O derived by trajectory analysis of the AMSOS data. Thus the large variability of H2O VMR during our flight is explained by a change of the winds in the lower mesosphere. This study shows that trajectory analysis can be applied in the mesosphere and is a powerful tool to understand the large variability in mesospheric H2O.

scholarly journals Study of Aerosol Influence on Nighttime Land Surface Temperature Retrieval Based on Two Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Caixia Gao ◽  
Enyu Zhao ◽  
Chuanrong Li ◽  
Yonggang Qian ◽  
Lingling Ma ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Vapor ◽  
Standard Deviation ◽  
Land Surface ◽  
Simulated Data ◽  
Mean Square ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Instrument Noise ◽  
Temperature Retrieval

The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.

Observations of Precipitation Size and Fall Speed Characteristics within Coexisting Rain and Wet Snow

2006 ◽  
Vol 45 (10) ◽  
pp. 1450-1464 ◽  
Author(s):  
Sandra E. Yuter ◽  
David E. Kingsmill ◽  
Louisa B. Nance ◽  
Martin Löffler-Mang
Keyword(s):  
Particle Size ◽  
Standard Deviation ◽  
Volume Fraction ◽  
Bimodal Distribution ◽  
Particle Sizes ◽  
Large Standard Deviation ◽  
Air Temperatures ◽  
Isothermal Layer ◽  
Wet Snow ◽  
Fall Speed

Abstract Ground-based measurements of particle size and fall speed distributions using a Particle Size and Velocity (PARSIVEL) disdrometer are compared among samples obtained in mixed precipitation (rain and wet snow) and rain in the Oregon Cascade Mountains and in dry snow in the Rocky Mountains of Colorado. Coexisting rain and snow particles are distinguished using a classification method based on their size and fall speed properties. The bimodal distribution of the particles’ joint fall speed–size characteristics at air temperatures from 0.5° to 0°C suggests that wet-snow particles quickly make a transition to rain once melting has progressed sufficiently. As air temperatures increase to 1.5°C, the reduction in the number of very large aggregates with a diameter > 10 mm coincides with the appearance of rain particles larger than 6 mm. In this setting, very large raindrops appear to be the result of aggregrates melting with minimal breakup rather than formation by coalescence. In contrast to dry snow and rain, the fall speed for wet snow has a much weaker correlation between increasing size and increasing fall speed. Wet snow has a larger standard deviation of fall speed (120%–230% relative to dry snow) for a given particle size. The average fall speed for observed wet-snow particles with a diameter ≥ 2.4 mm is 2 m s−1 with a standard deviation of 0.8 m s−1. The large standard deviation is likely related to the coexistence of particles of similar physical size with different percentages of melting. These results suggest that different particle sizes are not required for aggregation since wet-snow particles of the same size can have different fall speeds. Given the large standard deviation of fall speeds in wet snow, the collision efficiency for wet snow is likely larger than that of dry snow. For particle sizes between 1 and 10 mm in diameter within mixed precipitation, rain constituted 1% of the particles by volume within the isothermal layer at 0°C and 4% of the particles by volume for the region just below the isothermal layer where air temperatures rise from 0° to 0.5°C. As air temperatures increased above 0.5°C, the relative proportions of rain versus snow particles shift dramatically and raindrops become dominant. The value of 0.5°C for the sharp transition in volume fraction from snow to rain is slightly lower than the range from 1.1° to 1.7°C often used in hydrological models.

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