scholarly journals A Ground Validation Network for the Global Precipitation Measurement Mission

2011 ◽  
Vol 28 (3) ◽  
pp. 301-319 ◽  
Author(s):  
Mathew R. Schwaller ◽  
K. Robert Morris
Keyword(s):  
Attenuation Correction ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Retrieval Algorithm ◽  
Precipitation Data ◽  
Precipitation Measurement ◽  
Trmm Pr ◽  
Ground Validation ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA’s Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to “matchup” the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was −1.88 dBZ. The PR–GR bias was found to increase with the amount of PR attenuation correction applied, with the PR–GR bias reaching −3.07 dBZ in cases where the attenuation correction applied is >6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%–40%.

scholarly journals Evaluation of Radar Rainfall Products: Lessons Learned from the NASA TRMM Validation Program in Florida

2006 ◽  
Vol 23 (11) ◽  
pp. 1492-1505 ◽  
Author(s):  
Eyal Amitai ◽  
David A. Marks ◽  
David B. Wolff ◽  
David S. Silberstein ◽  
Brad L. Fisher ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Product Evaluation ◽  
Radar Data ◽  
Lessons Learned ◽  
Limiting Factors ◽  
Radar Rainfall ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Evaluation of the Tropical Rainfall Measuring Mission (TRMM) satellite observations is conducted through a comprehensive ground validation (GV) program. Since the launch of TRMM in late 1997, standardized instantaneous and monthly rainfall products are routinely generated using quality-controlled ground-based radar data adjusted to the gauge accumulations from four primary sites. As part of the NASA TRMM GV program, effort is being made to evaluate these GV products. This paper describes the product evaluation effort for the Melbourne, Florida, site. This effort allows us to evaluate the radar rainfall estimates, to improve the algorithms in order to develop better GV products for comparison with the satellite products, and to recognize the major limiting factors in evaluating the estimates that reflect current limitations in radar rainfall estimation. Lessons learned and suggested improvements from this 8-yr mission are summarized in the context of improving planning for future precipitation missions, for example, the Global Precipitation Measurement (GPM).

scholarly journals Sensitivity of 89–190-GHz Microwave Observations to Ice Particle Scattering

2020 ◽  
Vol 59 (7) ◽  
pp. 1195-1215
Author(s):  
Ruiyao Chen ◽  
Ralf Bennartz
Keyword(s):  
Radar Reflectivity ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Humidity Sounder ◽  
Slant Path ◽  
The One ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractThe sensitivity of microwave brightness temperatures (TBs) to hydrometeors at frequencies between 89 and 190 GHz is investigated by comparing Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) measurements with radar reflectivity profiles and retrieved products from the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (DPR). Scattering-induced TB depressions (ΔTBs), calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel, are compared with DPR-retrieved hydrometeor water path (HWP) and vertically integrated radar reflectivity ZINT. We also account for the number of hydrometeors actually visible in each MWHS-2 channel by weighting HWP with the channel’s cloud-free gas transmission profile and the observation slant path. We denote these transmission-weighted, slant-path-integrated quantities with a superscript asterisk (e.g., HWP*). The so-derived linear sensitivity of ΔTB with respect to HWP* increases with frequency roughly to the power of 1.78. A retrieved HWP* of 1 kg m−2 at 89 GHz on average corresponds to a decrease in observed TB, relative to a cloud-free background, of 11 K. At 183 GHz, the decrease is about 34–53 K. We perform a similar analysis using the vertically integrated, transmission-weighted slant-path radar reflectivity and find that ΔTB also decreases approximately linearly with . The exponent of 0.58 corresponds to the one we find in the purely DPR-retrieval-based ZINT–HWP relation. The observed sensitivities of ΔTB with respect to and HWP* allow for the validation of hydrometeor scattering models.

Deepening our understanding of shallow precipitation measurements from space

2020 ◽  
Author(s):  
Linda Bogerd ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
Remko Uijlenhoet
Keyword(s):  
Research Area ◽  
Level Ii ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Precipitation Type ◽  
Project Structure ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
The Impact

<p><span><span>Satellite-based remote sensing provides a unique opportunity for the estimation of global precipitation patterns. </span><span>In order to use this approach, it is crucial that the uncertainty in the satellite estimations is precisely understood. T</span><span>he retrieval</span><span> of high-latitude precipitation </span><span>(especially shallow precipitation) </span><span>remains challenging for satellite precipitation monitoring. </span><span>This </span><span>project</span><span> will quantify the quality of the precipitation estimations obtained from</span> <span>the Global Precipitation Measurement (GPM) mission, where the focus will be on the level II and III products.</span> <span>Initially, t</span><span>h</span><span>e </span><span>Netherlands </span><span>is chosen as research area, since it has an excellent infrastructure with both in-situ and remotely sensed ground-based precipitation measurements, </span><span>its flat topography,</span> <span>and the </span><span>frequent</span> <span>occurrence</span><span> of shallow precipitation events. The project will study the influence of precipitation type and the impact of the seasons on the accuracy </span><span>of the GPM products. </span><span>Hereafter, the project will focus on the physical causes behind the discrepancies between the GPM products and the ground validation</span><span>, </span><span>w</span><span>hich can be used to improve the </span><span>retrieval</span><span> algorithms. The </span><span>presentation</span><span> will outline the project structure and will demonstrate </span><span>the</span> <span>initial</span><span> results. </span></span></p>

scholarly journals Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

2021 ◽  
Vol 13 (22) ◽  
pp. 4565
Author(s):  
Maria Panfilova ◽  
Vladimir Karaev
Keyword(s):  
Wind Speed ◽  
Good Accuracy ◽  
Retrieval Algorithm ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Ku Band ◽  
Wide Swath

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.

Cross-validation of microwave snowfall products over the continental United States

2021 ◽  
Author(s):  
Kamil Mroz ◽  
Mario Montopoli ◽  
Giulia Panegrossi ◽  
Luca Baldini ◽  
Alessandro Battaglia ◽  
...  
Keyword(s):  
United States ◽  
Skill Score ◽  
Single Frequency ◽  
Retrieval Algorithm ◽  
Dual Frequency ◽  
Precipitation Measurement ◽  
Composite Product ◽  
Low Sensitivity ◽  
Global Precipitation Measurement ◽  
Global Precipitation

<p>In this talk, surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States. The analysis spans a period between Nov. 2014 and Sept. 2020 and covers the following products: the Dual-Frequency Precipitation Radar product (2A.GPM.DPR) and its single frequency counterparts (2A.GPM.Ka, 2A.GPM.Ku); GPM Combined Radar Radiometer Algorithm (2B.GPM.DPRGMI.CORRA); the CloudSat Snow Profile product (2C-SNOW-PROFILE) and two passive microwave retrievals i.e. the Goddard PROFiling algorithm (2A.GPM.GMI.GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). </p><p>The 2C-SNOW product has the highest Heidke Skill Score (HSS=75%) for detecting snowfall among all the analysed products. SLALOM ranks the second (60%) while the Ka-band products falls at the end of the spectrum, with the HSS of 10% only. Low detection capabilities of the DPR products are a result of its low sensitivity. All the GPM retrievals underestimate not only the snow occurances but also snowfall volumes. Underestimation by a factor of two is present for all the GPM products compared to MRMS data. Large discrepancies (RMSE of 0.7 to 1.5 mm/h) between space-borne and ground-based snowfall rate estimates can be attributed to the complexity of ice scattering properties and differences in the algorithms' assumptions.</p>

scholarly journals SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

2018 ◽  
Vol 10 (8) ◽  
pp. 1278 ◽  
Author(s):  
Jean-François Rysman ◽  
Giulia Panegrossi ◽  
Paolo Sanò ◽  
Anna Marra ◽  
Stefano Dietrich ◽  
...  
Keyword(s):  
False Alarm ◽  
Classification Scheme ◽  
Retrieval Algorithm ◽  
False Alarm Ratio ◽  
Water Path ◽  
Precipitation Measurement ◽  
Snow Water ◽  
Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI.

scholarly journals Data-Enabled Field Experiment Planning, Management, and Research Using Cyberinfrastructure

2015 ◽  
Vol 16 (3) ◽  
pp. 1155-1170 ◽  
Author(s):  
Ibrahim Demir ◽  
Helen Conover ◽  
Witold F. Krajewski ◽  
Bong-Chul Seo ◽  
Radosław Goska ◽  
...  
Keyword(s):  
Experiment Planning ◽  
Field Campaign ◽  
Resource Center ◽  
Weather Radars ◽  
Related Space ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Informatics Tools

Abstract In the spring of 2013, NASA conducted a field campaign known as Iowa Flood Studies (IFloodS) as part of the Ground Validation (GV) program for the Global Precipitation Measurement (GPM) mission. The purpose of IFloodS was to enhance the understanding of flood-related, space-based observations of precipitation processes in events that transpire worldwide. NASA used a number of scientific instruments such as ground-based weather radars, rain and soil moisture gauges, stream gauges, and disdrometers to monitor rainfall events in Iowa. This article presents the cyberinfrastructure tools and systems that supported the planning, reporting, and management of the field campaign and that allow these data and models to be accessed, evaluated, and shared for research. The authors describe the collaborative informatics tools, which are suitable for the network design, that were used to select the locations in which to place the instruments. How the authors used information technology tools for instrument monitoring, data acquisition, and visualizations after deploying the instruments and how they used a different set of tools to support data analysis and modeling after the campaign are also explained. All data collected during the campaign are available through the Global Hydrology Resource Center (GHRC), a NASA Distributed Active Archive Center (DAAC).

Sign in / Sign up

Export Citation Format

Share Document