ALBERT

Description: Dual tipping bucket gauges were operated at 16 sites in support of ground based precipitation measurements during Mid-latitude Continental Convective Clouds Experiment (MC3E). The experiment is conducted in North Central Oklahoma from April 22 through June 6, 2011. The gauge sites were distributed around Atmospheric Radiation Measurement (ARM) Climate Research facility where the minimum and maximum separation distances ranged from 1 to 12 km. This study investigates the rainfall variability by employing the stretched exponential function. It will focus on the quantitative assessment of the partial beam of the experiment area in both convective and stratiform rain. The parameters of the exponential function will also be determined for various events. This study is unique for two reasons. First is the existing gauge setup and the second is the highly convective nature of the events with rain rates well above 100 mm/h for 20 minutes. We will compare the findings with previous studies.

Description: Disdrometer based hydrometeor size distribution measurements have a wide range of application in radar meteorology among other disciplines. The low-cost laser optical disdrometer which became commercially available in 2005 opened a new era to study the variability of the hydrometeor size distribution. The pixel and footprint scale variability of the ground and spaceborne radar remote sensing of precipitation, respectively, has been investigated through a network of Parsivel disdrometers. At the same timE, the cross comparison of impact-type, two-dimensional video and laser-optical disdrometers highlighted the relative strengths and weaknesses of each type of disdrometer. The Parsivel instrument tended to underestimate the number of small drops less than 0.8 mm in diameter quite severely, while the size of drops larger than 2.0 mm in diameter were overestimated at heavy rain quite frequently. Through a close collaboration with the manufacturer, these shortcomings were attributed to the inexpensive laser device. The manufacturer has just released a beta version of Parsivel2. The preliminary comparisons with old and new versions of Parsivel, impact type disdrometer and rain gauges showed a noticeable improvement of the Parsivel at both small and large drop end. This presentation will demonstrate the evaluation of the new unit and its potential impact on radar rainfall relations, including polarimetric and high frequency radar rainfall.

Description: Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

Description: Dual tipping bucket gauges were operated at 16 sites in support of ground based precipitation measurements during Mid-latitude Continental Convective Clouds Experiment (MC3E). The experiment is conducted in North Central Oklahoma from April 22 through June 6, 2011. The gauge sites were distributed around Atmospheric Radiation Measurement (ARM) Climate Research facility where the minimum and maximum separation distances ranged from 1 to 12 km. This study investigates the rainfall variability by employing the stretched exponential function. It will focus on the quantitative assessment of the partial beam of the experiment area in both convective and stratiform rain. The parameters of the exponential function will also be determined for various events. This study is unique for two reasons. First is the existing gauge setup and the second is the highly convective nature of the events with rain rates well above 100 mm h-1 for 20 minutes. We will compare the findings with previous studies.

Description: Proper characterization of the precipitation drop size distribution (DSD) is integral to providing realistic and accurate space- and ground-based precipitation retrievals. Current technology allows for the development of DSD products from a variety of platforms, including disdrometers, vertical profilers and dual-polarization radars. Up to now, however, the dissemination or availability of such products has been limited to individual sites and/or field campaigns, in a variety of formats, often using inconsistent algorithms for computing the integral DSD parameters, such as the median- and mass-weighted drop diameter, total number concentration, liquid water content, rain rate, etc. We propose to develop a framework for the generation and dissemination of DSD characteristic products using a unified structure, capable of handling the myriad collection of disdrometers, profilers, and dual-polarization radar data currently available and to be collected during several upcoming GPM Ground Validation field campaigns. This DSD super-structure paradigm is an adaptation of the radar super-structure developed for NASA s Radar Software Library (RSL) and RSL_in_IDL. The goal is to provide the DSD products in a well-documented format, most likely NetCDF, along with tools to ingest and analyze the products. In so doing, we can develop a robust archive of DSD products from multiple sites and platforms, which should greatly benefit the development and validation of precipitation retrieval algorithms for GPM and other precipitation missions. An outline of this proposed framework will be provided as well as a discussion of the algorithms used to calculate the DSD parameters.

Description: Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Description: Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Description: A comparative study of raindrop size distribution measurements has been conducted at NASA's Goddard Space Flight Center where the focus was to evaluate the performance of the upgraded laser-optical OTT Particle Size Velocity (Parsivel2; P2) disdrometer. The experimental setup included a collocated pair of tipping-bucket rain gauges, OTT Parsivel (P1) and P2 disdrometers, and Joss-Waldvogel (JW) disdrometers. Excellent agreement between the two collocated rain gauges enabled their use as a relative reference for event rain totals. A comparison of event total showed that the P2 had a 6%absolute bias with respect to the reference gauges, considerably lower than the P1 and JW disdrometers. Good agreement was also evident between the JW and P2 in hourly raindrop spectra for drop diameters between 0.5 and 4 mm. The P2 drop concentrations mostly increased toward small sizes, and the peak concentrations were mostly observed in the first three measurable size bins. The P1, on the other hand, underestimated small drops and overestimated the large drops, particularly in heavy rain rates. From the analysis performed, it appears that the P2 is an improvement over the P1 model for both drop size and rainfall measurements. P2 mean fall velocities follow accepted terminal fall speed relationships at drop sizes less than 1 mm. As a caveat, the P2 had approximately 1ms21 slower mean fall speed with respect to the terminal fall speed near 1 mm, and the difference between the mean measured and terminal fall speeds reduced with increasing drop size. This caveat was recognized as a software bug by the manufacturer and is currently being investigated.

Description: Characteristics of the NASA NPOL S-band dual-polarimetric radar are presented including its operating characteristics, field configuration, scanning capabilities and calibration approaches. Examples of precipitation science data collections conducted using various scan types, and associated products, are presented for different convective system types and previous field campaign deployments. Finally, the NASA NPOL radar location is depicted in its home base configuration within the greater Wallops Flight Facility precipitation research array supporting NASA Global Precipitation Measurement Mission ground validation.

Description: Weather radars, recording information about precipitation around the globe, will soon be significantly upgraded. Most of today s weather radars transmit and receive microwave energy with horizontal orientation only, but upgraded systems have the capability to send and receive both horizontally and vertically oriented waves. These enhanced "dual-polarimetric" (DP) radars peer into precipitation and provide information on the size, shape, phase (liquid / frozen), and concentration of the falling particles (termed hydrometeors). This information is valuable for improved rain rate estimates, and for providing data on the release and absorption of heat in the atmosphere from condensation and evaporation (phase changes). The heating profiles in the atmosphere influence global circulation, and are a vital component in studies of Earth s changing climate. However, to provide the most accurate interpretation of radar data, the radar must be properly calibrated and data must be quality controlled (cleaned) to remove non-precipitation artifacts; both of which are challenging tasks for today s weather radar. The DP capability maximizes performance of these procedures using properties of the observed precipitation. In a notable paper published in 2005, scientists from the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) at the University of Oklahoma developed a method to calibrate radars using statistically averaged DP measurements within light rain. An additional publication by one of the same scientists at the National Severe Storms Laboratory (NSSL) in Norman, Oklahoma introduced several techniques to perform quality control of radar data using DP measurements. Following their lead, the Topical Rainfall Measuring Mission (TRMM) Satellite Validation Office at NASA s Goddard Space Flight Center has fine-tuned these methods for specific application to the weather radar at Kwajalein Island in the Republic of the Marshall Islands, approximately 2100 miles southwest of Hawaii and 1400 miles east of Guam in the tropical North Pacific Ocean. This tropical oceanic location is important because the majority of rain, and therefore the majority of atmospheric heating, occurs in the tropics where limited ground-based radar data are available.