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Abstract
The spatial and temporal variability of convection during the North American Monsoon Experiment (NAME) was examined via analysis of three-dimensional polarimetric radar data. Terrain bands were defined as the Gulf of California (over water) and elevations of 0–500 m above mean sea level (MSL; coastal plain), 500–1500 m MSL, and >1500 m MSL. Convective rainfall over the Gulf typically featured the smallest values of median volume diameter (D 0) regardless of rain rate. Gulf convection also contained reduced precipitation-sized ice water mass but proportionally more liquid water mass compared to convection over land. These maritime characteristics were magnified during disturbed meteorological regimes, which typically featured increased precipitation over the Gulf and adjacent coastal plain. Overall, the results suggest increased reliance on warm-rain collision and coalescence at the expense of ice-based precipitation growth processes for convective rainfall over the Gulf, relative to the land. Over land D 0, ice, and liquid water mass all increased with decreasing terrain elevation, suggesting intensification of convection as it moved off the Sierra Madre Occidental. The results are consistent with the hypothesis that both warm-rain and ice-based rainfall processes play important roles in precipitation formation over land. Coastal-plain convection underwent microphysical modifications during disturbed meteorological regimes that were similar to Gulf convection, but the changes were less dramatic. High-terrain convection experienced little microphysical variability regardless of meteorological regime.
Abstract
The spatial and temporal variability of convection during the North American Monsoon Experiment (NAME) was examined via analysis of three-dimensional polarimetric radar data. Terrain bands were defined as the Gulf of California (over water) and elevations of 0–500 m above mean sea level (MSL; coastal plain), 500–1500 m MSL, and >1500 m MSL. Convective rainfall over the Gulf typically featured the smallest values of median volume diameter (D 0) regardless of rain rate. Gulf convection also contained reduced precipitation-sized ice water mass but proportionally more liquid water mass compared to convection over land. These maritime characteristics were magnified during disturbed meteorological regimes, which typically featured increased precipitation over the Gulf and adjacent coastal plain. Overall, the results suggest increased reliance on warm-rain collision and coalescence at the expense of ice-based precipitation growth processes for convective rainfall over the Gulf, relative to the land. Over land D 0, ice, and liquid water mass all increased with decreasing terrain elevation, suggesting intensification of convection as it moved off the Sierra Madre Occidental. The results are consistent with the hypothesis that both warm-rain and ice-based rainfall processes play important roles in precipitation formation over land. Coastal-plain convection underwent microphysical modifications during disturbed meteorological regimes that were similar to Gulf convection, but the changes were less dramatic. High-terrain convection experienced little microphysical variability regardless of meteorological regime.
Abstract
The Cabauw Experimental Site for Atmospheric Research (CESAR) observatory hosts a unique collection of instruments related to precipitation measurement. The data collected by these instruments are stored in a database that is freely accessible through a Web interface. The instruments present at the CESAR site include three disdrometers (two on the ground and one at 200 m above ground level), a dense network of rain gauges, three profiling radars (1.3, 3.3, and 35 GHz), and an X-band Doppler polarimetric scanning radar. In addition to these instruments, operational weather radar data from the nearby (∼25 km) De Bilt C-band Doppler radar are also available. The richness of the datasets available is illustrated for a rainfall event, where the synergy of the different instruments provides insight into precipitation at multiple spatial and temporal scales. These datasets, which are freely available to the scientific community, can contribute greatly to our understanding of precipitation-related atmospheric and hydrologic processes.
Abstract
The Cabauw Experimental Site for Atmospheric Research (CESAR) observatory hosts a unique collection of instruments related to precipitation measurement. The data collected by these instruments are stored in a database that is freely accessible through a Web interface. The instruments present at the CESAR site include three disdrometers (two on the ground and one at 200 m above ground level), a dense network of rain gauges, three profiling radars (1.3, 3.3, and 35 GHz), and an X-band Doppler polarimetric scanning radar. In addition to these instruments, operational weather radar data from the nearby (∼25 km) De Bilt C-band Doppler radar are also available. The richness of the datasets available is illustrated for a rainfall event, where the synergy of the different instruments provides insight into precipitation at multiple spatial and temporal scales. These datasets, which are freely available to the scientific community, can contribute greatly to our understanding of precipitation-related atmospheric and hydrologic processes.
Abstract
A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge measurements from recent historical data. Then this relationship is applied to real-time satellite estimates when gauge data are not yet available. This new scheme is tested over the United States with six years of precipitation estimates from two real-time satellite products [i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product 3B42RT and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH)] and a gauge analysis dataset [i.e., the CPC unified analysis]. The first 4-yr period was used as the training period to establish a satellite–gauge relationship, which was then applied to the last 2 yr as the correction period, during which gauge data were withheld for training but only used for evaluation. This approach showed that satellite biases were reduced by 70%–100% for the summers in the correction period. In addition, even when sparse networks with only 600 or 300 gauges were used during the training period, the biases were still reduced by 60%–80% and 47%–63%, respectively. The results also show a limitation in this approach as it tends to overadjust both light and strong events toward more intermediate rain rates.
Abstract
A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge measurements from recent historical data. Then this relationship is applied to real-time satellite estimates when gauge data are not yet available. This new scheme is tested over the United States with six years of precipitation estimates from two real-time satellite products [i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product 3B42RT and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH)] and a gauge analysis dataset [i.e., the CPC unified analysis]. The first 4-yr period was used as the training period to establish a satellite–gauge relationship, which was then applied to the last 2 yr as the correction period, during which gauge data were withheld for training but only used for evaluation. This approach showed that satellite biases were reduced by 70%–100% for the summers in the correction period. In addition, even when sparse networks with only 600 or 300 gauges were used during the training period, the biases were still reduced by 60%–80% and 47%–63%, respectively. The results also show a limitation in this approach as it tends to overadjust both light and strong events toward more intermediate rain rates.
Abstract
A new multiplatform multisensor satellite rainfall estimation technique is proposed in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to advect microwave (MW)-derived precipitation estimates along cloud motion streamlines and to further adjust the rainfall rates using local cloud classification. The main objective of the Rain Estimation using Forward-Adjusted advection of Microwave Estimates (REFAME) is to investigate whether inclusion of GEO-IR information can help to improve the advected MW precipitation rate as it gets farther in time from the previous MW overpass. The technique comprises three steps. The first step incorporates a 2D cloud tracking algorithm to capture cloud motion streamlines through successive IR images. The second step classifies cloudy pixels to a number of predefined clusters using brightness temperature (Tb) gradients between successive IR images along the cloud motion streamlines in combination with IR cloud-top brightness temperatures and textural features. A mean precipitation rate for each cluster is calculated using available MW-derived precipitation estimates. In the third step, the mean cluster precipitation rates are used to adjust MW precipitation intensities advected between available MW overpasses along cloud motion streamlines. REFAME is a flexible technique, potentially capable of incorporating diverse precipitation-relevant information, such as multispectral data. Evaluated over a range of spatial and temporal scales over the conterminous United States, the performance of the full REFAME algorithm compared favorably with products incorporating either no cloud tracking or no intensity adjustment. The observed improvements in root-mean-square error and especially in correlation coefficient between REFAME outputs and ground radar observations demonstrate that the new approach is effective in reducing the uncertainties and capturing the variation of precipitation intensity along cloud advection streamlines between MW sensor overpasses. An extended REFAME algorithm combines the adjusted advected MW rainfall rates with infrared-derived precipitation rates in an attempt to capture precipitation events initiating and decaying during the interval between two consecutive MW overpasses. Evaluation statistics indicate that the extended algorithm is effective to capture the life cycle of the convective precipitation, particularly for the interval between microwave overpasses in which precipitation starts or ends.
Abstract
A new multiplatform multisensor satellite rainfall estimation technique is proposed in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to advect microwave (MW)-derived precipitation estimates along cloud motion streamlines and to further adjust the rainfall rates using local cloud classification. The main objective of the Rain Estimation using Forward-Adjusted advection of Microwave Estimates (REFAME) is to investigate whether inclusion of GEO-IR information can help to improve the advected MW precipitation rate as it gets farther in time from the previous MW overpass. The technique comprises three steps. The first step incorporates a 2D cloud tracking algorithm to capture cloud motion streamlines through successive IR images. The second step classifies cloudy pixels to a number of predefined clusters using brightness temperature (Tb) gradients between successive IR images along the cloud motion streamlines in combination with IR cloud-top brightness temperatures and textural features. A mean precipitation rate for each cluster is calculated using available MW-derived precipitation estimates. In the third step, the mean cluster precipitation rates are used to adjust MW precipitation intensities advected between available MW overpasses along cloud motion streamlines. REFAME is a flexible technique, potentially capable of incorporating diverse precipitation-relevant information, such as multispectral data. Evaluated over a range of spatial and temporal scales over the conterminous United States, the performance of the full REFAME algorithm compared favorably with products incorporating either no cloud tracking or no intensity adjustment. The observed improvements in root-mean-square error and especially in correlation coefficient between REFAME outputs and ground radar observations demonstrate that the new approach is effective in reducing the uncertainties and capturing the variation of precipitation intensity along cloud advection streamlines between MW sensor overpasses. An extended REFAME algorithm combines the adjusted advected MW rainfall rates with infrared-derived precipitation rates in an attempt to capture precipitation events initiating and decaying during the interval between two consecutive MW overpasses. Evaluation statistics indicate that the extended algorithm is effective to capture the life cycle of the convective precipitation, particularly for the interval between microwave overpasses in which precipitation starts or ends.
Abstract
The bright band (BB) is a layer of enhanced reflectivity due to melting of aggregated snow and ice crystals. The locally high reflectivity causes significant overestimation in radar precipitation estimates if an appropriate correction is not applied. The main objective of the current study is to develop a method that automatically corrects for large errors due to BB effects in a real-time national radar quantitative precipitation estimation (QPE) product. An approach that combines the mean apparent vertical profile of reflectivity (VPR) computed from a volume scan of radar reflectivity observations and an idealized linear VPR model was used for computational efficiency. The methodology was tested for eight events from different regions and seasons in the United States. The VPR correction was found to be effective and robust in reducing overestimation errors in radar-derived QPE, and the corrected radar precipitation fields showed physically continuous distributions. The correction worked consistently well for radars in flat land regions because of the relatively uniform spatial distributions of the BB in those areas. For radars in mountainous regions, the performance of the correction is mixed because of limited radar visibility in addition to large spatial variations of the vertical precipitation structure due to underlying topography.
Abstract
The bright band (BB) is a layer of enhanced reflectivity due to melting of aggregated snow and ice crystals. The locally high reflectivity causes significant overestimation in radar precipitation estimates if an appropriate correction is not applied. The main objective of the current study is to develop a method that automatically corrects for large errors due to BB effects in a real-time national radar quantitative precipitation estimation (QPE) product. An approach that combines the mean apparent vertical profile of reflectivity (VPR) computed from a volume scan of radar reflectivity observations and an idealized linear VPR model was used for computational efficiency. The methodology was tested for eight events from different regions and seasons in the United States. The VPR correction was found to be effective and robust in reducing overestimation errors in radar-derived QPE, and the corrected radar precipitation fields showed physically continuous distributions. The correction worked consistently well for radars in flat land regions because of the relatively uniform spatial distributions of the BB in those areas. For radars in mountainous regions, the performance of the correction is mixed because of limited radar visibility in addition to large spatial variations of the vertical precipitation structure due to underlying topography.
Abstract
Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary.
To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.
Abstract
Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary.
To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.
