Remote Sensing of Precipitation Posters
Presiding: J McCollum, NOAA CICS/ESSIC and University of Maryland; M Steiner, Princeton University
H23A-01 1330h
Effect of Wind and Out-of-Levelness on Rain Gauge Catch -- Revisiting an Old Problem
Gauge measurements are widely used as the true rainfall reaching the land surface for a variety of applications, ranging from calibration of remotely sensed rainfall estimates to water budget studies. The quality of rain gauge data, however, may be affected by human or animal interference, mechanical or electric failures, or debris that settled inside the collector funnel hindering the rainwater from reaching the measuring device in a timely fashion or at all. Other sources of potential rainfall measurement errors relate to the wind effect on the gauge catch and whether the instrument's orifice is leveled. These two particular effects are revisited here. It is shown that out-of-level gauges may either miss or catch too much rainwater in windy conditions, with associated errors potentially amounting to tens of percent of the total catch.
H23A-02 1330h
Analysis of Small-Scale Rainfall Variability in South Louisiana Using a Dense Rain Gauge Network
Despite the technological advances that have been made in the area of radar hydrology, accurate and reliable radar-derived rainfall amounts over scales relevant to hydrologic applications are not readily available. One of the difficult problems causing this situation is the inadequacy of the rain gauge point measurements commonly used for evaluation of radar rainfall estimates. The use of gauge observations to validate area-averaged radar rainfall estimates makes it imperative to take into considerations two main issues: scale differences between radar and rain gauges, and rainfall variability across scales smaller than radar resolutions. The current study is an attempt to understand and possibly characterize rainfall small-scale variability and its impact on the validation of radar-rainfall estimates. The study site is a dense rain gauge network in south Louisiana. The network, which has been in operation since 2004, includes a total of 13 dual-rain gauge sites with separations distances ranging from less than one km to about few kilometers. The network also includes an acoustic Joss-Waldvogel disdrometer, which provides continuous estimates of raindrop size spectra. The study focuses on estimation of the spatial correlation function, the variogram, and the errors associated with areal rainfall estimates from gauge measurements. It is worth noting that Louisiana is considered one of the wettest states in the US, and to the best of the authors' knowledge, information about rainfall small-scale variability in this region has not been previously available.
H23A-03 1330h
Space-Time Variability of Reflectivity Estimated by Vertically Pointing and Scanning Radars during TEFLUN-B
One of the goals of the NASA Global Precipitation Mission (GPM) is to establish ground sites that will produce precipitation estimates and uncertainties throughout columns of the atmosphere to be used as reference points for satellite precipitation algorithm development and validation. The fundamental problem with developing these columns of estimated precipitation is that different ground based instruments have different spatial and temporal resolutions as well as different measurement error characteristics. This would not be a problem if the precipitation was not varying in time and space. But since the precipitation is variable at different temporal and spatial resolutions, the uncertainties of the Quantitative Precipitation Estimate (QPE) from each instrument will be a combination of the instrument measurement error and the spatiotemporal variability of the precipitation. Thus, an important goal of the ground validation for GPM will be to quantify the measurement and sampling errors of each instrument and combine the data into an error model to evaluate the satellite algorithm performance. This study addresses the spatiotemporal variability of precipitation observed by a vertically pointing profiling radar operating at 915 MHz and the Melbourne, FL, WSR-88D NOAA weather radar located 36 km away and observing the reflectivity around the profiler site. While the profiling radar produced reflectivity estimates every minute with a vertical resolution of 100 m, the scanning radar completed volume scans every 5 minutes and had a vertical resolution over the profiler site of about 630 m. For each of the 21 rain events that occurred during the TEFLUN-B campaign (August-September 1998), the profiler 1-minute reflectivity estimates were processed to produce 5-minute mean and variance reflectivity profiles. As expected, the profiler reflectivity variance was less for stratiform rain regimes than for convective rain regimes. Also, the variance of the scanning radar reflectivity over a 10 x 10 km domain around the profiler site increased as the profiler reflectivity variance increased, demonstrating a correlation between the temporal reflectivity variance observed by a profiling radar with the spatiotemporal reflectivity variance observed by a scanning radar. The correlation provides a measure of sampling variability within different rain types that can be extended over the coverage umbrella of the scanning radar. The statistics from the 21 rain events will be presented at the conference.
H23A-04 1330h
Evaluation of Rainfall Estimates from Polarimetrically Tuned Z-R Relations and Vertical Profile of Reflectivity Corrections
Estimating surface rainfall from volume scan radar data continues to be an important and challenging research problem in radar meteorology. Many studies have indicated that rainfall estimates can be improved by incorporating vertical profile of reflectivity (VPR) corrections. Another highly-potential improvement in radar rainfall estimation has come from the emergence and subsequent application of dual-polarized scanning radar to operational use, utilizing polarimetrically-tuned radar reflectivity to rainfall (Z-R) relationships. While both solutions are different in application, each promises (in theory) to yield better estimates of precipitation over the entire radar sampling area than traditional reflectivity-only radar rainfall estimation techniques. It is the goal of this study to evaluate and characterize the performance of each of these methods by comparing to gauge-bias corrected rainfall amounts derived from standard reflectivity measurements. Volume scan radar data collected over 27 days in July 2002 from the NASA S-Band polarimetric Doppler Radar (NPOL) in Southwestern Florida provides an excellent dataset for this study.
H23A-05 1330h
Estimating Watershed Accumulated Precipitation From Radar and Gage Data
Radar-derived precipitation estimates are potentially valuable for studies whereground weather station rainfall data is not available at sufficient spatial density to account for precipitation variation over a study area. Stage IV precipitation analyses are one of the national products produced by the National Centers for Environmental Prediction (NCEP). This product combines data from Doppler weather radar precipitation estimates and surface rain gages to produce an estimate of rainfall coverage. Stage IV analyses are distributed via NCEP FTP servers in a Gridded Binary (GRIB) format, which is not directly useful for calculating a total accumulation. Therefore, the purpose of this project was to develop a computer program that can process data from this GRIB file and produce a useful result in terms of total rainfall over a watershed described in a Geographic Information Systems (GIS) format. A number of mathematical and computational issues were resolved in order to make this program efficient and effective. Tests were conducted to validate the program's approximation against surface gage measurements, and potential extensions of the algorithm were identified.
H23A-06 1330h
Analysis of Radar-Rainfall Error and its Effect on Runoff Predictions
Recent years have witnessed significant advances in the development of operational radar-rainfall products. These products are desirable for several hydrologic applications such as flood forecasting and rainfall-runoff modeling. It is recognized that radar-rainfall estimates are associated with unknown uncertainties. The nature of these uncertainties and their impact on the prediction accuracy of hydrologic models is not fully understood. The complexity of the spatial and temporal structure of radar-rainfall error has lead most of the previous studies have to approach this problem using simulation-based analyses where the effects of model-related errors can be separated from those of the radar-rainfall input. The present study presents a preliminary analysis of the uncertainties of operational radar-rainfall products and how they propagate into rainfall-runoff models. The study uses the NWS Multi-sensor Precipitation Estimator (MPE) radar-rainfall products over the Goodwin Creek experimental watershed. The products have hourly temporal resolution and are available over the HRAP grid (4x4 km2 approximately). Surface rainfall observations from a dense rain gauge network in the watershed are used to analyze the error characteristics of the radar products. The MPE radar data are used as input to a semi-distributed hydrologic model to simulate runoff response during 12 storms recorded in 2001. The study focuses on the effect of three different radar error sources: systematic error (bias), random error, and temporal and spatial resolution effects. Initial results indicate that, for the study watershed, the bias and random components of the radar error have the most significant impact on prediction accuracy of the hydrologic model.
H23A-07 1330h
A continuous spatial-temporal stochastic rainfall model based on historical data
The goal of the work is to model continuous spatial-temporal rainfall characteristics on the watershed scale, and present the model as a tool for further analysis of rainfall properties such as spatial and temporal average and extreme values of rainfall intensities, and for simulating rainfall scenarios to be used by models that study the response and evolution of rainfall-sensitive systems. The modeled rainfall process is event-based and has a hierarchical structure: rainfall occurs in storms, which in turn consist of rain generating patches, each having a random size and a random rainfall intensity. Randomly located patches form a spatial Boolean field; the storm is modeled by a spatial field moving across the region of interest. Fitting the spatially-temporally continuous model makes use of relations between spatial objects (rain patches) and their linear transects, which correspond to historical records at fixed rain gauges. Hourly historical data at eight stations in Alabama are used for illustrating estimation, properties and possible applications of the model.
H23A-08 1330h
Scale Dependence of Spatial Statistics of TRMM PR-derived Rainfall and a Stochastic Fractional Diffusion Model
It is common knowledge that rain statistics vary in a non-trivial manner with the space-time scales over which the rain rate field is averaged. A recently developed stochastic dynamical rainfall model based on a fractional kinetic equation of the diffusion type quantitatively accounts for many aspects of spatial and temporal variability of rain expressed in terms of the second moment statistics of area-averaged precipitation rate observed in surface radar measurements, such as GATE and TOGA-COARE. In particular, the model predicts that the variance of area-averaged rain rate σ2(L) has a power law singularity, as the averaging length scale L tends to zero. In the present work the model is tested with satellite rainfall data, specifically the TRMM Precipitation Radar data over the tropical western Pacific Ocean.
H23A-09 1330h
Satellite Rainfall Retrieval from Microwave Radiometry: Effect of Spatial Inhomogeneity in Bias Reduction
The spatial variability of rainfall within the sensor field of view coupled to the non-linearity in the physical connection between microwave brightness temperature and rainfall contributes to the so-called beam-filling error. This study interprets this error as an inherent error that depends only on the rainfall characteristics and sensor responses. Thus, the inherent error cannot be reduced by using more accurate cloud-radiative models, but by the detail knowledge of the rainfall characteristics, which is needed in the first place. This study explores the relation between the spatial variability of the high frequency radiometric data and the rainfall characteristics that may be used for reducing the inherent error in the instantaneous rainfall estimations from passive microwave sensors. Analyses of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) data suggest that the inherent error can be examined in terms of the coefficient of variation (CVr, rain rate standard deviation divided by mean within a field of view). It is also observed that the spatial variability of TMI 85.5 GHz brightness temperature (CVtb) shows good correlations with CVr, especially for higher rain rates. We showed that both CVtb and CVr are useful in classifying rain type. It implies that the spatial inhomogeneity and vertical distribution of rainfall combine to contribute to the uncertainty in the retrievals. In synthetic retrievals, we demonstrate the spatial variability of the high frequency Tb (CVtb) reduces the retrieval error (bias) from -5.3% to 2.4% for the east Pacific and from -12.4% to -5.4% for the west Pacific for the winter season.
H23A-10 1330h
Comparison of TRMM Rainfall and Daily Gauge Data in Thailand
Daily rainfall data collected from more than a hundred gauges over Thailand have been used to study the climatology, seasonal and non-seasonal variations of Thailand rainfall and to compare rainfall characteristics with rainfall estimated from Tropical Rainfall Measuring Mission (TRMM) rain algorithms. In this study, Thailand regions are separated into the northern (north of 12°) and southern part. Climatologically, Thailand has a tropical climate, influenced by monsoon winds that vary in direction according to the season. The northern region rains mostly during the JJA season and the southern region rains dominantly during the SON season. Statistical analyses showed that the gauge data is very close to gauge analyses produced by the Global Precipitation Climatology Center (GPCC) and TRMM satellite and gauge merged analysis (3B43) estimates, respectively. In contrast, TRMM microwave calibrated IR (3B42) estimates are much higher than the rain gauge measurements. The gauge data are binned at daily 1 x 1 degree resolution. Preliminary results show that TRMM 3B42 has about 20% false alarm rate and about 5% non-detection rate. The daily rainfall data from TRMM microwave imager (TMI) and precipitation radar (PR) are also extracted from the TRMM mission index to calculate the false alarm and non-detection rates for different seasons. Moreover, the Normalized Difference Vegetation Index (NDVI) data is used to study the effect of land cover changes to rainfall over Thailand. The results show that the northern part has the most vegetation greenness in the SON season and there is an increasing trend of NDVI in the past 20 years over Thailand.
H23A-11 1330h
An Integrated Approach (TRMM, MODIS, AVHRR, and Rain Gauge) for Assessment of Precipitation in Arid Areas: A Case Study from the Eastern Desert of Egypt
Water shortages are major obstacles to sustainable development and a cause for poverty in arid and semiarid countries. In these domains often the case, the appropriate systems (precipitation networks) that are needed to estimate precipitation on a regional scale are absent. We developed an integrated methodology to address this problem using data sets that are available on a global scale. We developed an integrated approach to improve estimates of renewable water resources. The approach utilizes the following data sets (1) TRMM-3B42V6 to extract 3-hourly precipitation data, (2) daily AVHRR data for soil moisture and NDVI measurements, (3) METEOSAT-7 for monitoring cloud movement, and (4) rain gauge data for ground truthing. Our approach entails identifying rain storm events from TRMM data. Following the identification of the events, we verify the individual events by examining the cloud patterns, examining the temporal variations in NDVI and soil moisture, and through comparisons with rain gauge data. For the year 1998, we examined in a GIS environment the following: TRMM scenes (2920 scenes), AVHRR data (365 scenes), METEOSAT (8760 scenes), and available rain gauge data. Findings indicate: (1) A general correspondence between TRMM data and rain gauge data, (2) A progressive increase in NDVI measurements following precipitation (peak after ~10 days), and (3) instantaneous increase in soil moisture. A similar (yet with a more restricted data set) exercise was conducted in year 1994, where a major flood occurred.
H23A-12 1330h
Satellite Rainfall Probability and Estimation. Application to the West Africa During the 2004 Rainy Season
The international program AMMA (African Monsoon Multidisciplinary Analysis) is in intensive phase from the beginning of 2005 until 2007, over West Africa. It has a crucial need of precipitation estimations at scales ranging from the small basin to the regional scale, and from instantaneous values to monthly totals. This need includes estimations of the errors corresponding to each scale. Moreover, no sufficient ground data are available in West Africa to satisfy the AMMA time and space scale needs. To fulfil those, a satellite precipitation algorithm is developed. In order to get relevant information in tropical regions with very sporadic rainfall, the use of a high time sampling, which can only be provided by the geostationary satellites, is required. Unfortunately, although statistical information on rainfall occurrence can be obtained from these geo-satellite data, instantaneous rain rate intensity cannot be derived from the available infrared or visible channels. Because of their close relationship with rainfall phenomenon, active or passive microwave data from low orbit satellites are necessary. In this study the TRMM Precipitation Radar (PR), which provides instantaneous values of ground rainfall intensity, is used. The training of a neural network system is performed using six months collocated data during the 2004 rainy season of Meteosat-8 (MSG) infrared data and TRMM PR algorithm rain estimates. Several Meteosat-derived parameters, including radiances and space-time characteristics, constitute the entries, while Precipitation Radar 2A25 rain information (rain/no rain) are used to train the feed forward neural network. The outputs are considered as Rainfall Probabilities. Rainfall estimations from these outputs are obtained by multiplying these Rainfall Probabilities by a Potential Rainfall intensity. Considering that the Potential Rainfall intensity depends on the geographical localisation and the phase of the seasonal cycle, its value has to vary in space and time. These Potential Rainfall intensities are calibrated by a reference dataset of rainfall estimations, and have been calculated thanks to an upscaling formula. The reference dataset used for this study was the 1dd GPCP product because of its global geographical cover and its appropriate temporal resolution. From these two products (Rainfall Probability and daily Potential Rainfall intensity) estimated rainfall intensity fields are provided at the fine geostationary satellite time and space resolutions. By taking into account the Rainfall Probability, the algorithm presented here may be interpreted as a way to improve and downscale the rainfall estimations deduced from the reference dataset.
H23A-13 1330h
Spatial and Temporal Patterns of Remotely-Sensed and Field-Measured Rainfall in Southern California
Quantification of spatial and temporal patterns of rainfall is an important step toward developing regional hydrological models. However, traditionally used rain gauge data are sparse and do not always provide adequate spatial representation of rainfall. In this study, we evaluated the daily 1-degree resolution remotely-sensed atmospheric precipitation data provided by Global Precipitation Climatology Project (GPCP) as an alternative to rain gauge-measured data. We analyzed data from the watersheds of southern California during the period of 1996-2003, focusing on the comparison of patterns of spatial, seasonal, and interannual rainfall dynamics. We used Empirical Orthogonal Functions to discern the patterns of precipitation and atmospheric circulation at different time scales, from synoptic to interannual. The correlation between the daily rain gauge-measured and remotely-sensed precipitation was poor and the resulting patterns of remotely-sensed precipitation are different than the temporal patterns of precipitation accumulated by rain gauges. These differences likely result from the fact that the precipitable water concentration measured by satellites is not always highly correlated to rainfall reaching the earth surface. Differences in the spatial resolution and coverage of the two methods and the differential influence of orographic effects and wind patterns on each also contribute to low correlations. We conclude that daily remotely-sensed precipitation produced at GPCP is not currently appropriate for use in assessing fine-scale hydrological processes in arid zones like southern California, and would not be a recommended surrogate for event-based hydrologic modeling. At the same time, the interannual variabilities of remotely-sensed and gauge-measured precipitation were highly correlated and the regional patterns of gauge-measured and remotely-sensed precipitation variability were similar. Therefore, remotely-sensed precipitation data may be appropriate for use in long-term regional hydrologic or climate modeling. Both data sets showed that precipitation generally decreases from the northern to the southern watersheds. At interannual time-scale, the rainfall is related to the ENSO cycle. At synoptic time-scales, the rainfall patterns in southern California result from atmospheric moisture transport from the south-southwest.
H23A-14 1330h
Characterization of Rainfall Intensity and Storm Lifecycles in South Florida
Understanding tropical thunderstorm cirrus anvil microphysics and their relation to the lifecycle and intensity of the thunderstorm's convective core will help improve modeling of these clouds and their environment. The Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL-FACE) was conducted over south Florida 2002. Measurements collected during the experiment provide an opportunity to study rainfall intensity and lifecycle characteristics of storms observed in South Florida. Data collected from the University of North Dakota's Citation research aircraft and the NASA S-Band polarimetric Doppler radar (NPOL) are used in this study. Storm events were selected from days throughout July that had coincident observations (space and time) between the Citation and NPOL. The intensity and general lifecycles of the thunderstorms and their convective cores are examined to find the maximum reflectivity, anvil height, 10 dBZ height, and 40 dBZ height. These values are then compared with the microphysical properties derived from Citation data, which includes the mean, mean volume, and median volume diameters, as well as the particle concentration. In a previous study of an event that occurred on 16 July, a positive correlation was found between the mean volume diameter, the maximum reflectivity, and maximum 40 dBZ height, as well as a slight negative correlation between the particle concentration and maximum anvil height. This study will expand on those results by examining several more cases during July. Through these comparisons, we are trying to determine if there are any relationships between the pre-storm environment, storm intensity, and properties of the cirrus anvil cloud of tropical convective systems.
H23A-15 1330h
The Validation of TRMM TMI and PR Precipitation Estimates at Climatological Scales
The Tropical Rainfall Measuring Mission (TRMM) has been collecting data for over eight years. One of the original goals of TRMM was to ascertain uncertainties in satellite-derived monthly precipitation estimates from the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR). The accuracy of TRMM monthly estimates, however, are limited by the sampling frequency of the satellite, which varies as a function of latitude from one to three samples per day within a latitudinal range of 40 N and 40 S. Monthly integrations of data collected by TRMM sensors are determined statistically from the total number of samples collected each month. Sampling errors in the range of ~8-12% month over the tropical oceans were expected, in addition to the retrievals errors associated with the actual measurement and estimation of instantaneous areal rainfall from space. Consequently, whereas sampling errors result from a lack of information about the state of the atmosphere when the satellite is not overhead, retrieval errors are mostly attributed to the physical modeling of an instantaneous areal rate from the real time data collected by the TRMM sensors. This climatological validation study, based on a methodology developed by Fisher (2004), decomposes the sampling and retrieval errors associated with TRMM TMI and PR monthly estimates into two distinct error distributions. This method uses high-resolution ground data, sub-sampled at satellite overpass times. The sub-sampled rain estimate is then assumed to contain a sampling error equivalent to the satellite. The sampling error variance can then be partly parameterized based on the statistical differences between the rain estimate, R0, computed at all times and sub-sampled rain parameter, RS. Similarly, the retrieval error distribution is statistically parameterized in terms of the statistical variance between the satellite estimate, S and the sub-sampled ground estimate, RS. An annual bias factor is also computed for both sampling and retrievals that is weighted by the annual climatology, computed from the validation parameter, R0. The formulation of the bias used in this study represents a recent modification on the methodology of Fisher (2004). The TRMM GV site in Melbourne Florida was used as a regional test of this proposed methodology. The TMI and PR monthly precipitation estimates from version 5 and 6 are validated over a four-year period (1998-2001) using both the Melbourne NEXRAD radar and a large network of 97 rain gauges distributed within the radar domain that extends out 150 km from the radar. A 2 x 2 deg. gridded region was selected for this study, which only considered 0.5 deg. grid boxes where there existed rain gauges. Monthly rainfall was first computed at 0.5 deg. resolution and then averaged over the full grid space. Error statistics were computed at both 0.5 and 2.0 degrees for each year of the study. Random errors were characterized by the coefficient of variation (CV=std/mean). At the 2x 2 deg. scale, these statistics showed the PR CVsam about 25% higher than the TMI, whereas PR CVret were about 30% lower than the TMI. Version 5 shows a large positive summertime bias. Another interesting result showed a negative bias between the sub-sampled radar and gauge, which may be attributable to spatial sampling differences between the two sensors.
H23A-16 1330h
Validation for the Tropical Rainfall Measuring Mission: Lessons Learned and Future Plans
The Tropical Rainfall Measuring Mission (TRMM) was launched in November 1997 and was a highly regarded and successful mission. A major component of the TRMM program was its Ground Validation (GV) program. Through dedicated research and hard work by many groups, both the GV and satellite-retrieved rain estimates have shown a convergence at key GV sites, lending credibility to the global TRMM estimates. To be sure, there are some regional differences between the various satellite estimates themselves which still need to be addressed; however, it can be said with some certainty that TRMM has provided a high-quality, long-term climatological data set for researchers that provides errors on the order of 10-20%, rather than pre-TRMM-era error estimates on the order of 50-10%. The TRMM GV program's main operational task was to provide rainfall products for four sites: Darwin, Australia; Houston, Texas; Kwajalein, Republic of the Marshall Islands; and, Melbourne, Florida. A comparison between TRMM GV (Version 5) and satellite (Version 6) rain intensity estimates is presented. The gridded satellite product (3G-68) will be compared to GV Level II rain-intensity and -type maps. The 3G-68 product represents a 0.5 deg x 0.5 deg data grid providing estimates of rain intensities from the TRMM Precipitation Radar, Microwave Imager and Combined algorithms. The comparisons will be classified according to geographical type (land, coast or ocean). The convergence of the GV and satellite estimates bodes well for expectations for the proposed Global Precipitation Measurement (GPM) program, but it is now well understood that providing uncertainties of the estimates is perhaps more important than convergence on its own. Further, while TRMM originally focused on monthly and climatological validation, future precipitation missions should concentrate on instantaneous validation in order to avoid inevitable and large sampling errors.
H23A-17 1330h
The Global Precipitation Measurement (GPM) Mission Front Range Pilot Project (FRPP)
Successful ground validation of rainfall, microphysical, and heating products from the Global Precipitation Measurement (GPM) core and constellation satellites will rely on a multi-faceted combination of many ground-based instruments at land and ocean "Supersites", as well as possible constellation validation sites. The FRPP, held along northern Colorado's Front Range during May-July 2004, provided a low cost opportunity to demonstrate the valuable, synergistic combination of dual-frequency, dual-polarization scanning Doppler radars (CSU-CHILL S-Band and NOAA-ETL X-Band), dual-frequency profiling radars (AL and ETL profilers at the Platteville and Boulder Atmospheric Observatories), surface disdrometers (ETL and AL Joss-Waldvogel and CSU's 2D-Video) and rain gauges. The program had three main objectives: (1) Dual-wavelength dual-polarization scanning radar DSD and rain rate estimate intercomparison, validation, and error characterization, (2) Dual-wavelength profiler demonstration in the Supersite concept, and (3) Rain rate and drop size distribution characterization in the context of Supersite observations and rainfall regimes. This poster will show results from the FRPP, in the context of both case studies demonstrating the advantages of planned Supersite instrumentation, and demonstration of case study composites separated into regimes according to their rain type and environmental characteristics. A template and preliminary demonstration of error analysis for GV data will also be presented, which will allow quantitative measurement uncertainties to be propagated from particular GV instrumentation to the satellite pixel resolution.
http://radarmet.atmos.colostate.edu/gpm
H23A-18 1330h
A National Test bed for Hydrometeorological and Severe Storm Research and Development
The National Severe Storms Laboratory, in collaboration with the NWS Office of Hydrologic Development, is currently establishing a national hydrometeorological test bed for the research and development of multisensor applications. A key component of the test bed is the National Mosaic and Quantitative precipitation estimation (NMQ) system. The NMQ will allow the creation of new applications and the dissemination of high-resolution quantitative precipitation estimation products seamlessly across North America for flash flood detection and prediction, fresh water resource management, and severe weather detection and prediction. The NMQ project will function as a community based research and development program that encompasses integration of multiple observational data streams (currently existing and future), prototype and technique development environment, and real time verification and performance assessments on a national scale across small time and space resolutions. The NMQ system will capitalize on the rapid real-time communication of base-level WSR-88D radar data, satellite, surface, and NWP data. The NMQ system will utilize two high performance Linux computer clusters connected to a large bandwidth data hub. One cluster will serve as a development and testing platform within a Joint Applications Development Environment (JADE) that will allow field personal, university researchers and NOAA scientists to develop and assess, in real time, new QPE and short-term QPF techniques, as well as multi-sensor severe weather applications. The second cluster (currently deployed) functions as a pseudo-operational environment providing QPE and severe weather products with a minimal resolution of 1 km updated every 5-10 minutes seamlessly across the U.S.
H23A-19 1330h
Using Surface Humidity Measurement to Estimate Atmospheric Moisture Availability for Extreme Rainstorms
Surface humidity measurements are used to estimate atmospheric moisture availability for Probable Maximum Precipitation (PMP) estimation. In particular, a pseudo-adiabatic dewpoint profile is assumed to estimate precipitable water from surface 12-hour persisting dewpoint. This assumption is employed in estimating both the precipitable water for observed extreme rainstorms, and the maximum possible precipitable water used for scaling observed rainfall to reflect maximum conditions (a concept known as moisture maximization). This assumption was reevaluated using pairs of radiosonde and surface airways observations for the central United States. The results show the deficiencies in estimating atmospheric moisture availability using surface humidity measurements with this approach. The pseudo-adiabatic assumption systematically overestimates precipitable water for observed extreme rainstorms; the overestimation of maximum moisture conditions is even greater. As a result, the PMP estimate based on the moisture maximization concept is larger than expected based on the empirical assessment of atmospheric moisture availability. To assess the overestimation of PMP estimates, a natural logarithm formula was derived from a 23-year climatology of maximum precipitable water and 12-hr persisting dewpoint, to better represent moisture conditions for extreme conditions. Using the proposed formula for moisture maximization suggests that the pseudo-adiabatic assumption overestimates PMP by about 6% on average.
H23A-20 1330h
Development of a High Resolution Precipitation Research Facility in the Northern Plains
The Department of Atmospheric Science at the University of North Dakota is developing a high resolution precipitation research facility in the northern plains. The site is located about 60 km south-southeast of Grand Forks, North Dakota. The research site resides on the Nature Conservancy Glacial Ridge Prairie Restoration Project, a 24,000 acre property that is currently being restored to natural prairie and wetlands from existing farmland. The extensive area of the property provides a unique opportunity to study precipitation variability over temporal scales of seconds to an annual time frame and spatial scales ranging from meters to tens of kilometers. The research facility will include a dense network of rain gauges, several disdrometers, a vertical wind profiler, snow sensors, and microwave radiometer. Other measurements including longwave/shortwave radiation, aerosol, and surface boundary conditions are also planned for the site. The research facility is designed to compliment existing hydrologic research activities at Glacial Ridge. The site is ideally located to provide surface precipitation observations for radar studies using the UND C-Band polarimetric Doppler weather radar located in Grand Forks and with the Mayville WSR-88D Doppler weather radar located in Mayville, North Dakota. An overview of the facility, planned research activities, and observations will be given in the presentation.
H23A-21 1330h
Improving Radar Snowfall Measurements Using a Video Disdrometer
A video disdrometer has been recently developed at NASA/Wallops Flight Facility in an effort to improve surface precipitation measurements. The recent upgrade of the UND C-band weather radar to dual-polarimetric capabilities along with the development of the UND Glacial Ridge intensive atmospheric observation site has presented a valuable opportunity to attempt to improve radar estimates of snowfall. The video disdrometer, referred to as the Rain Imaging System (RIS), has been deployed at the Glacial Ridge site for most of the 2004-2005 winter season to measure size distributions, precipitation rate, and density estimates of snowfall. The RIS uses CCD grayscale video camera with a zoom lens to observe hydrometers in a sample volume located 2 meters from end of the lens and approximately 1.5 meters away from an independent light source. The design of the RIS may eliminate sampling errors from wind flow around the instrument. The RIS has proven its ability to operate continuously in the adverse conditions often observed in the Northern Plains. The RIS is able to provide crystal habit information, variability of particle size distributions for the lifecycle of the storm, snowfall rates, and estimates of snow density. This information, in conjunction with hand measurements of density and crystal habit, will be used to build a database for comparisons with polarimetric data from the UND radar. This database will serve as the basis for improving snowfall estimates using polarimetric radar observations. Preliminary results from several case studies will be presented.
H23A-22 1330h
AMSR-E Validation of Winter Precipitation Over the Baltic Sea
Ongoing research related to the evaluation of AMSR-E data over the Baltic Sea region will be presented. In particular, the AMSR-E 89 GHz frequency's sensitivity to light, frozen winter precipitation will be discussed. Comparisons between AMSR-E and simulated brightness temperatures and scattering signatures will be highlighted (the modeled results are derived from ground-based radar data from Gotland Island, Sweden). The microphysical model used to derive optical properties and subsequent brightness temperatures from 3-D radar reflectivity volumes will also be discussed. The model relies on a "size ratio" of melted frozen droplet diameters compared to liquid droplet diameters below the melting layer, which guarantees consistency between the precipitation rates of the frozen and liquid hydrometeors. This method has previously produced good results when applied to warm-season precipitation cases over the Baltic Sea. Initial results indicate that AMSR-E brightness temperatures compare well to the modeled brightness temperatures for a variety of different winter precipitation events. The optimal size ratio that produces the best results may differ between the various cases, though. Lastly, the precipitation rates derived from AMSR-E and the modeled radar-based data will be compared to the standard, rain-gauge adjusted precipitation rates produced by the BALTEX Radar Data Center.