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At least 199 records · Page 11

SAIL Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed the second ARM Mobile Facility (AMF2) near Crested Butte, Colorado for the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. The SAIL campaign occurred from September 1, 2021, to June 15 2023. To study the water cycle in the East River Watershed, ARM deployed a vertically pointing Ka-band ARM Zenith radar (KAZR) and a scanning X-band precipitation radar managed by Colorado State University (CSU XPRECIP), as shown in Figure 1.

54 ENVIRONMENTAL SCIENCES↗

Measured electric field intensities near electric cloud discharges detected by the Kennedy Space Center's Lightning Detection and Ranging System, LDAR

For a summer thunderstorm, for which simultaneous, airborne electric field measurements and Lightning Detection and Ranging (LDAR) System data was available, measurements were coordinated to present a picture of the electric field intensity near cloud electrical discharges detected by the LDAR System. Radar precipitation echos from NOAA's 10 cm weather radar and measured airborne electric field intensities were superimposed on LDAR PPI plots to present a coordinated data picture of thunderstorm activity.

Poehler, H. A.↗

On Rainfall Modification by Major Urban Areas: Observations from Space-borne Rain Radar on TRMM - Part 1

This study represents one of the first published attempts to identify rainfall modification by urban areas using satellite-based rainfall measurements. Data from the first space-based rain-radar, the Tropical Rainfall Measuring Mission's (TRMM) Precipitation Radar, are employed. Analysis of the data enables identification of rainfall patterns around Atlanta, Montgomery, Nashville, San Antonio, Waco, and Dallas during the warm season. Results reveal an average increase of -28% in monthly rainfall rates within 30-60 kilometers downwind of the metropolis with a modest increase of 5.6% over the metropolis. Portions of the downwind area exhibit increases as high as 51%. The percentage chances are relative to an upwind CONTROL area. It was also found that maximum rainfall rates in the downwind impact area can exceed the mean value in the upwind CONTROL area by 48%-116%. The maximum value was generally found at an average distance of 39 km from the edge of the urban center or 64 km from the center of the city. These results are consistent with METROMEX studies of St. Louis almost two decades ago and more recent studies near Atlanta. Future work will investi(yate hypothesized factors causing rainfall modification by urban areas. Additional work is also needed to provide more robust validation of space-based rain estimates near major urban areas. Such research has implications for urban planning, water resource management, and understanding human impact on the environment.

Shepherd, J. Marshall↗

Integrated Multi-Satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

Integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi-Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global-scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

Rainfall↗

Integrated Multi-satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

An integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Obser­vatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi­Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global­scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

Pierre-Emmanuel Kirstette↗

Comparison of TRMM 2A25 Products Version 6 and Version 7 with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem was addressed in a previous paper by comparison of 2A25 version 6 (V6) product with reference values derived from NOAA/NSSL's ground radar-based National Mosaic and QPE system (NMQ/Q2). The primary contribution of this study is to compare the new 2A25 version 7 (V7) products that were recently released as a replacement of V6. This new version is considered superior over land areas. Several aspects of the two versions are compared and quantified including rainfall rate distributions, systematic biases, and random errors. All analyses indicate V7 is an improvement over V6.

Kirstetter, Pierre-Emmanuel↗

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primaryTRMMinstruments: theTRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation sigma among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3mm day(exp -1) for the deep tropical ocean beltbetween 10 deg N and 10 deg S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by sigma is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (~16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

(GPCP) Global Precipitation Project analysis↗

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. The cornerstone of the GPM mission is the deployment of a Core Observatory in a 65 deg non-Sun-synchronous orbit to serve as a physics observatory and a transfer standard for inter-calibration of constellation radiometers. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The first space-borne dual-frequency radar will provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles needed for improving precipitation retrievals from passive microwave sensors. The combined use of DPR and GMI measurements will place greater constraints on radiometer retrievals to improve the accuracy and consistency of precipitation estimates from all constellation radiometers. The GPM constellation is envisioned to comprise five or more conical-scanning microwave radiometers and four or more cross-track microwave sounders on operational satellites. NASA and the Japan Aerospace Exploration Agency (JAXA) plan to launch the GPM Core in July 2013. NASA will provide a second radiometer to be flown on a partner-provided GPM Low-Inclination Observatory (L10) to improve near real-time monitoring of hurricanes and mid-latitude storms. NASA and the Brazilian Space Program (AEB/IPNE) are currently engaged in a one-year study on potential L10 partnership. JAXA will contribute to GPM data from the Global Change Observation Mission-Water (GCOM-W) satellite. Additional partnerships are under development to include microwave radiometers on the French-Indian Megha-Tropiques satellite and U.S. Defense Meteorological Satellite Program (DMSP) satellites, as well as cross-track scanning humidity sounders on operational satellites such as the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), POES, the NASA/NOAA Joint Polar Satellite System (JPSS), and EUMETSAT MetOp satellites. Data from Chinese and Russian microwave radiometers may also become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. Relative to current data products, GPM's "nextgeneration" precipitation products will be characterized by: (1) more accurate instantaneous precipitation estimate (especially for light rain and cold-season solid precipitation), (2) more frequent sampling by an expanded constellation of microwave radiometers including operational humidity sounders over land, (3) intercalibrated microwave brightness temperatures from constellation radiometers within a unified framework, and (4) physical-based precipitation retrievals from constellation radiometers using a common a priori hydrometeor database constrained by combined radar/radiometer measurements provided by the GPM Core Observatory. An overview of the GPM mission concept, the U.S. GPM program status and updates on international science collaborations on GPM will be presented.

Hou, Arthur Y.↗

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗

Interannual Variability of the Tropical Water Cycle: Capabilities in the TRMM Era and Challenges for GPM

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30" NE) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Although it is well documented that El Nino-Southern Oscillation (ENSO) events with marked SST changes over the tropical oceans, produce significant regional changes in precipitation, water vapor, and radiative fluxes in the tropics, we still cannot yet adequately quantify the associated net integrated changes to water and heat balance over the entire tropical oceanic or land sectors. Robertson et al., [2001 GRL] for example, showed that substantial disagreement exists among contemporary satellite estimates of interannual variations in tropical rainfall that are associated with SST changes. Berg et al., [2002 J. Climate] have documented the distinct differences between precipitation structure over the eastern and western Pacific ITCZ and noted how various satellite precipitation algorithms may respond quite differently to ENSO modulations of these precipitation regimes. Resolving this uncertainty is important since precipitation and latent heat release variations over land and ocean sectors are key components of the tropical heat balance in its most aggregated form. Rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) averaged over the tropical oceans have not solved this issue and, in fact, show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. In this paper we will focus on findings that uncertainties in microphysical assumptions necessitated by the single-frequency PR measurement pose difficulties for detecting climate-related precipitation signals. Recent work has shown that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series, yet the PR rainfall interannual variability (and attenuation derived predominantly from reflectivity) differs even in sign. We will explore these apparent inconsistencies and detail their impact on estimates of how ENSO events perturb the tropical rainfall. We will place these results in perspective by considering requirements for precipitation accuracy for global climate variability and change studies involving ENSO, monsoon dynamics and variations, and climate model improvement and validation. The discussion will conclude with an assessment of the implications of these findings for Global Precipitation Mission (GPM) requirements.

Robertson, Franklin R.↗

Meteorological Observations and System Performance From the NASA D3R's First 5 Years

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was conceived and developed to support ground validation (GV) operations of the Global Precipitation Measurement (GPM) mission. The D3R operates in the same frequencies bands, Ku- and Ka-band, as GPMs dual-frequency precipitation radar enabling direct comparisons of microphysical observations of precipitation. To support the GPM GVmission, D3R substantively participated in four field campaigns in North America with diverse geographic features covering both winter and summer conditions.

Radar↗

Interannual Variability of Tropical Rainfall as Seen From TRMM

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30deg N/S) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Although it is well documented that El Nino-Southern Oscillation (ENSO) events with marked SST changes over the tropical oceans produce significant regional changes in precipitation, water vapor, and radiative fluxes in the tropics, we still cannot yet adequately quantify the associated net integrated changes to water and heat balance over the entire tropical oceanic or land sectors. Resolving this uncertainty is important since precipitation and latent heat release variations over land and ocean sectors are key components of the tropical heat balance in its most aggregated form. Rainfall estimates from the Version 5 Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) averaged over the tropical oceans have not solved this issue and, in fact, show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. In this paper we will focus on findings that uncertainties in microphysical assumptions necessitated by the single-frequency PR measurement pose difficulties for detecting climate-related precipitation signals. Recent work has shown that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series, yet the PR rainfall interannual variability (and attenuation derived predominantly from reflectivity) differs even in sign. We will explore these apparent inconsistencies and examine changes in new TRMM Version 6 retrievals. To place these results in a tropical water balance perspective we also examine interannual variations in evaporation over the tropical oceans made from TRMM and SSM/I (Special Sensor Microwave Imager) measurements of surface winds and humidity. Evaporation estimates from reanalysis and several global model experiments will also be compared to the TRMM findings and evaluated for consistency. The ability to detect regional shifts in freshwater flux over the oceans (equivalently, integrated moisture convergence) and moisture transport will be discussed.

Robertson, Franklin R.↗

Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects.

GPM↗

A Range Compensating Feed Motion Concept for Spaceborne Radar

Tropical cyclones can cause major loss of both life and property, so that improvements in forecasting motion, intensity, and rainfall are needed. Such forecasting requires accurate measurements of the current state of the cyclone. Ground-based Doppler radars have long been used as an effective means of monitoring severe precipitating storms. Because of the oceanic nature of tropical cyclones, remote monitoring from space is desirable. Recently, the Precipitation Radar (PR) [1] aboard the Tropical Rainfall Measuring Mission (TRMM) [2] satellite has demonstrated an unprecedented capacity for 3-D imaging of precipitating storms. Nonetheless, due to the relatively long TRMM return cycle (less than once per day) the value of PR data has primarily been limited to the understanding of climatological properties of tropical cyclones [3]. The return cycle can be substantially reduced by sensing from a geostationary platform.

reflector antennas↗

GPM Mission Overview and U.S. Science Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission to unify and advance precipitation measurements from a constellation of research and operational sensors to provide "next-generation" precipitation products [1-2]. Water is fundamental to life on Earth. Knowing where and how much rain and snow falls globally is vital to understanding how weather and climate impact both our environment and Earth's water and energy cycles, including effects on agriculture, fresh water availability, and responses to natural disasters. Since rainfall and snowfall vary greatly from place to place and over time, satellites can provide more uniform observations of rain and snow around the globe than ground instruments, especially in areas where surface measurements are difficult. Relative to current global rainfall products, GPM data products will be characterized by: (l) more accurate instantaneous precipitation measurements (especially for light rain and cold-season solid precipitation), (2) more frequent sampling by an expanded constellation of domestic and international microwave radiometers including operational humidity sounders, (3) intercalibrated microwave brightness temperatures from constellation radiometers within a unified framework, and (4) physical-based precipitation retrievals from constellation radiometers using a common a priori cloud/hydrometeor database derived from GPM Core sensor measurements. The cornerstone of the GPM mission is the deployment of a Core Observatory in a unique 65 non-Sun-synchronous orbit to serve as a physics observatory and a reference standard to unify precipitation measurements by a constellation of dedicated and operational passive microwave sensors. The design of the GPM Core Observatory is an advancement of the Tropical Rainfall Measuring Mission (TRMM)'s highly successful rain-sensing package. The Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a multichannel (l0-183 GHz) GPM Microwave Radiometer (GMI). Since light rain and falling snow account for a significant fraction of precipitation occurrence in middle and high latitudes, the GPM instruments extend the capabilities of the TRMM sensors to detect falling snow, measure light rain, and provide, for the first time, quantitative estimates of microphysical properties of precipitation particles. The combined use of DPR and GMI measurements will place greater constraints on possible solutions to radiometer retrievals to improve the accuracy and consistency of precipitation retrievals from all constellation radiometers. The GMI uses 13 different microwave channels to observe energy from the different types of precipitation through clouds for estimating everything from heavy to light rain and for detecting falling snow. As the satellite passes over Earth, the GMI constantly scans a region 885 kilometers across. The Ball Aerospace and Technology Corporation built the GMI under contract with NASA Goddard Space Flight Center. The DPR provides three-dimensional information about precipitation particles derived from reflected energy by these particles at different heights within the cloud system. The two frequencies of the DPR also allow the radar to infer the sizes of precipitation particles and offer insights into a storm's physical characteristics. The Ka-band frequen~y scans across a region of 125 kilometers and is nested within the wider scan of the Ku-band frequency of 245 kilometers. The Japan Aerospace and Exploration Agency (JAXA) and Japan's National Institute of Information and Communications Technology (NICT) built the DPR. The Core Observatory satellite will fly at an altitude of 253 miles (407 kilometers) in a non-Sun-synchronous orbit that covers the Earth from 65 S to 65 N - from about the Antarctic Circle to the Arctic Circle. The GPM Core Observatory is being developed and tested at NASA Goddard Space Flight Center. Once complete, a Japanese H-lIA rocket will carry thPM Core Observatory into orbit from Tanegashima Island, Japan in 2014. The GPM constellation is envisioned to comprise 8 or more microwave sensors provided by partners, including both conical imagers and cross-track sounders. GPM is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA). Additional partnerships are under development to include microwave radiometers on the French-Indian Megha-Tropiques satellite and U.S. Defense Meteorological Satellite Program (DMSP) satellites, as well as humidity sounders or precipitation sensors on operational satellites such as the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), NOAA-NASA Joint Polar Satellite System (JPSS) satellites, European MetOp satellites, and DMSP follow-on sensors. In addition, data from Chinese and Russian microwave radiometers may be available through international cooperation under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). GPM's next-generation global precipitation data will lead to scientific advances and societal benefits in the following areas: (1) Improved knowledge of the Earth's water cycle and its link to climate change (2) New insights into precipitation microphysics, storm structures and large-scale atmospheric processes (3) Better understanding of climate sensitivity and feedback processes (4) Extended capabilities in monitoring and predicting hurricanes and other extreme weather events (5) Improved forecasting capabilities for natural hazards, including floods, droughts and landslides. (6) Enhanced numerical prediction skills for weather and climate (7) Better agricultural crop forecasting and monitoring of freshwater resources. An overview of the GPM mission concept and science activities in the United States, together with an update on international collaborations in radiometer intercalibration and ground validation, will be presented.

Hou, Arthur Y.↗

Seasonal Precipitation Classification during Surface Atmosphere Integrated Field Laboratory Campaign

The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, conducted from September 2021 to June 2023 in Crested Butte, Colorado, aimed to characterize precipitation processes in the Upper Colorado River Basin (UCRB). This increased observations of snowfall accumulation in this hydrologically significant watershed would be useful for quantitative precipitation estimates (QPE). Therefore, the Surface Quantitative Precipitation Estimate (SQUIRE) product was developed using the ARM-supported Colorado State University (CSU) X-band Precipitation Radar. Although SQUIRE will be only released for snowfall, by categorizing precipitation types, users can effectively utilize relevant datasets under diverse meteorological conditions. Moreover, the dataset facilitates validation of the QPE product and the analysis of seasonal variations in precipitation types at the surface. Hydrometeors classes are organized based on their phase and physical characteristics mapping the CSU (both winter Summer) and Py-ART classifications into four groups. 1. Liquid Precipitation: includs drizzle, rain, and large raindrops. 2. Frozen Snow and Ice : Pure Snow, combining ice crystals, aggregates, and vertically oriented ice structures. 3. Dense and Large frozen hydrometeors: including low- and high-density graupel and dry hail. 4.Melting: Wet Snow and Melting Hail, hydrometeors exhibiting both liquid and frozen characteristics.

54 ENVIRONMENTAL SCIENCES↗

Methods of Attenuation Correction for Dual-Wavelength and Dual-Polarization Weather Radar Data

In writing the integral equations for the median mass diameter and number concentration, or comparable parameters of the raindrop size distribution, it is apparent that the forms of the equations for dual-polarization and dual-wavelength radar data are identical when attenuation effects are included. The differential backscattering and extinction coefficients appear in both sets of equations: for the dual-polarization equations, the differences are taken with respect to polarization at a fixed frequency while for the dual-wavelength equations, the differences are taken with respect to frequency at a fixed polarization. An alternative to the integral equation formulation is that based on the k-Z (attenuation coefficient-radar reflectivity factor) parameterization. This-technique was originally developed for attenuating single-wavelength radars, a variation of which has been applied to the TRMM Precipitation Radar data (PR). Extensions of this method have also been applied to dual-polarization data. In fact, it is not difficult to show that nearly identical equations are applicable as well to dualwavelength radar data. In this case, the equations for median mass diameter and number concentration take the form of coupled, but non-integral equations. Differences between this and the integral equation formulation are a consequence of the different ways in which attenuation correction is performed under the two formulations. For both techniques, the equations can be solved either forward from the radar outward or backward from the final range gate toward the radar. Although the forward-going solutions tend to be unstable as the attenuation out to the range of interest becomes large in some sense, an independent estimate of path attenuation is not required. This is analogous to the case of an attenuating single-wavelength radar where the forward solution to the Hitschfeld-Bordan equation becomes unstable as the attenuation increases. To circumvent this problem, the equations can be expressed in the form of a final-value problem so that the recursion begins at the far range gate and proceeds inward towards the radar. Solving the problem in this way traditionally requires estimates of path attenuation to the final gate: in the case of orthogonal linear polarizations, the attenuations at horizontal and vertical polarizations (same frequency) are required while in the dual-wavelength case, attenuations at the two frequencies (same polarization) are required.

Meneghini, R.↗