The NASA Decadal Survey Observing-System Study for Aerosols and Clouds,Convection, and Precipitation (ACCP)
No abstract available
Braun, Scott A.↗
Engineering topics
Petersen, W.
Publications and source records attributed to Petersen, W..
Translating the Physics of Snowfall to Radar-Based Validation of GPM
No abstract available
Petersen, W.↗
OLYMPEX Data Workshop: GPM View
OLYMPEX Primary Objectives: Datasets to enable: (1) Direct validation over complex terrain at multiple scales, liquid and frozen precip types, (a) Do we capture terrain and synoptic regime transitions, orographic enhancements/structure, full range of precipitation intensity (e.g., very light to heavy) and types, spatial variability? (b) How well can we estimate space/time-accumulated precipitation over terrain (liquid + frozen)? (2) Physical validation of algorithms in mid-latitude cold season frontal systems over ocean and complex terrain, (a) What are the column properties of frozen, melting, liquid hydrometeors-their relative contributions to estimated surface precipitation, transition under the influence of terrain gradients, and systematic variability as a function of synoptic regime? (3) Integrated hydrologic validation in complex terrain, (a) Can satellite estimates be combined with modeling over complex topography to drive improved products (assimilation, downscaling) [Level IV products] (b) What are capabilities and limitations for use of satellite-based precipitation estimates in stream/river flow forecasting?
GPM↗
GPM Level 1 Science Requirements: Science and Performance Viewed from the Ground
No abstract available
Hydrology↗
Dataset Analysis to Support Verification of GPM Science Requirements
No abstract available
Precipitation↗
Gpm Level 1 Science Requirements: Science and Performance Viewed from the Ground
GPM meets Level 1 science requirements for rain estimation based on the strong performance of its radar algorithms. Changes in the V5 GPROF algorithm should correct errors in V4 and will likely resolve GPROF performance issues relative to L1 requirements. L1 FOV Snow detection largely verified but at unknown SWE rate threshold (likely < 0.5 –1 mm/hr/liquid equivalent). Ongoing work to improve SWE rate estimation for both satellite and GV remote sensing.
Precipitation↗
Toward a Framework for Systematic Error Modeling of NASA Spaceborne Radar 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 space-born passive and active microwave measurement") for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a three-month data sample in the southern part of US. The primary contribution of this study is the presentation of the detailed steps required to derive trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relics on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors arc revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall rate estimates from other sensors onboard low-earth orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.
Kirstettier, Pierre-Emmanual↗
Climate Change and Tropical Total Lightning
While global warming is regarded as a fact by many in the scientific community, its future impact remains a challenge to be determined and measured. The International Panel on Climate Change (IPCC) assessment report (IPCC, 2007) shows inconclusive answers on global rainfall trends and general agreement on a future drier climate with increased global warming. The relationship between temperature, humidity and convection is not linear and is strongly dependent on regional scale features, such as topography and land cover. Furthermore, the relationship between convective lightning production (thunderstorms) and temperature is even more complicated, being subjected to the cloud dynamics and microphysics. Total lightning (intracloud and cloud-to-ground) monitoring is a relatively new field of observation. Global and tropical total lightning began to be more extensively measured by satellites in the mid 90s. In this scope, the Lightning Imaging Sensor (LIS) onboard of the Tropical Rainfall Measurement Mission (TRMM) has been operational for over 11 years. Here we address total lightning trends observed by LIS from 1998 to 2008 in different temporal (annual and seasonal) and spatial (large and regional) scales. The observed 11-year trends are then associate to different predicted/hypothesized climate change scenarios.
Albrecht, R.↗
The 20-22 January 2007 Snow Events over Canada: Microphysical Properties
One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve precipitation measurements in mid- and high-latitudes during cold seasons through the use of high-frequency passive microwave radiometry. Toward this end, the Weather Research and Forecasting (WRF) model with the Goddard microphysics scheme is coupled with a Satellite Data Simulation Unit (WRF-SDSU) that has been developed to facilitate over-land snowfall retrieval algorithms by providing a virtual cloud library and microwave brightness temperature (Tb) measurements consistent with the GPM Microwave Imager (GMI). This study tested the Goddard cloud microphysics scheme in WRF for snowstorm events (January 20-22, 2007) that took place over the Canadian CloudSAT/CALIPSO Validation Project (C3VP) ground site (Centre for Atmospheric Research Experiments - CARE) in Ontario, Canada. In this paper, the performance of the Goddard cloud microphysics scheme both with 2ice (ice and snow) and 3ice (ice, snow and graupel) as well as other WRF microphysics schemes will be presented. The results are compared with data from the Environment Canada (EC) King Radar, an operational C-band radar located near the CARE site. In addition, the WRF model output is used to drive the Goddard SDSU to calculate radiances and backscattering signals consistent with direct satellite observations for evaluating the model results.
Tao. W.K.↗
WRF Simulations of the 20-22 January 2007 Snow Events over Eastern Canada: Comparison with In-Situ and Satellite Observations
One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve cold season precipitation measurements in middle and high latitudes through the use of high-frequency passive microwave radiometry. For this, the Weather Research and Forecasting (WRF) model with the Goddard microphysics scheme is coupled with a satellite data simulation unit (WRF-SDSU) that has been developed to facilitate over-land snowfall retrieval algorithms by providing a virtual cloud library and microwave brightness temperature (Tb) measurements consistent with the GPM Microwave Imager (GMI). This study tested the Goddard cloud microphysics scheme in WRF for two snowstorm events, a lake effect and a synoptic event, that occurred between 20 and 22 January 2007 over the Canadian CloudSAT/CALIPSO Validation Project (C3VP) site in Ontario, Canada. The 24h-accumulated snowfall predicted by the WRF model with the Goddard microphysics was comparable to the observed accumulated snowfall by the ground-based radar for both events. The model correctly predicted the onset and ending of both snow events at the CARE site. WRF simulations capture the basic cloud properties as seen by the ground-based radar and satellite (i.e., CloudSAT, AMSU-B) observations as well as the observed cloud streak organization in the lake event. This latter result reveals that WRF was able to capture the cloud macro-structure reasonably well.
Shi, J. J.↗
Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond
The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models is expected to be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 ground processing algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area)
Goodman, Steven J.↗
Can High-resolution WRF Simulations Be Used for Short-term Forecasting of Lightning?
A number of research teams have begun to make quasi-operational forecast simulations at high resolution with models such as the Weather Research and Forecast (WRF) model. These model runs have used horizontal meshes of 2-4 km grid spacing, and thus resolved convective storms explicitly. In the light of recent global satellite-based observational studies that reveal robust relationships between total lightning flash rates and integrated amounts of precipitation-size ice hydrometeors in storms, it is natural to inquire about the capabilities of these convection-resolving models in representing the ice hydrometeor fields faithfully. If they do, this might make operational short-term forecasts of lightning activity feasible. We examine high-resolution WRF simulations from several Southeastern cases for which either NLDN or LMA lightning data were available. All the WRF runs use a standard microphysics package that depicts only three ice species, cloud ice, snow and graupel. The realism of the WRF simulations is examined by comparisons with both lightning and radar observations and with additional even higher-resolution cloud-resolving model runs. Preliminary findings are encouraging in that they suggest that WRF often makes convective storms of the proper size in approximately the right location, but they also indicate that higher resolution and better hydrometeor microphysics would be helpful in improving the realism of the updraft strengths, reflectivity and ice hydrometeor fields.
Goodman, S. J.↗