scholarly journals Observational Constraint for Precipitation in Extratropical Cyclones: Sensitivity to Data Sources

2018 ◽  
Vol 57 (4) ◽  
pp. 991-1009 ◽  
Author(s):  
Catherine M. Naud ◽  
James F. Booth ◽  
Matthew Lebsock ◽  
Mircea Grecu
Keyword(s):  
Microwave Radiometer ◽  
Precipitable Water ◽  
Retrieval Algorithm ◽  
Extratropical Cyclones ◽  
Precipitation Frequency ◽  
Categorical Analysis ◽  
Ground Clutter ◽  
Precipitation Measurement ◽  
Observational Uncertainty ◽  
Global Precipitation

AbstractUsing cyclone-centered compositing and a database of extratropical-cyclone locations, the distribution of precipitation frequency and rate in oceanic extratropical cyclones is analyzed using satellite-derived datasets. The distribution of precipitation rates retrieved using two new datasets, the Global Precipitation Measurement radar–microwave radiometer combined product (GPM-CMB) and the Integrated Multisatellite Retrievals for GPM product (IMERG), is compared with CloudSat, and the differences are discussed. For reference, the composites of AMSR-E, GPCP, and two reanalyses are also examined. Cyclone-centered precipitation rates are found to be the largest with the IMERG and CloudSat datasets and lowest with GPM-CMB. A series of tests is conducted to determine the roles of swath width, swath location, sampling frequency, season, and epoch. In all cases, these effects are less than ~0.14 mm h−1 at 50-km resolution. Larger differences in the composites are related to retrieval biases, such as ground-clutter contamination in GPM-CMB and radar saturation in CloudSat. Overall the IMERG product reports precipitation more often, with larger precipitation rates at the center of the cyclones, in conditions of high precipitable water (PW). The CloudSat product tends to report more precipitation in conditions of dry or moderate PW. The GPM-CMB product tends to systematically report lower precipitation rates than the other two datasets. This intercomparison provides 1) modelers with an observational uncertainty and range (0.21–0.36 mm h−1 near the cyclone centers) when using composites of precipitation for model evaluation and 2) retrieval-algorithm developers with a categorical analysis of the sensitivity of the products to PW.

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Dimensional Structure ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

Understanding performance of GPROF precipitation retrievals over the Netherlands in relation to precipitation characteristics as derived from ground-based dual-polarization radars

2021 ◽  
Author(s):  
Linda Bogerd ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
Remko Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Background Radiation ◽  
A Priori ◽  
Dual Frequency ◽  
Dual Polarization ◽  
Ground Clutter ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Precipitation Events

<p>The Global Precipitation Measurement mission (GPM) is one of the recent efforts to provide satellite-based global precipitation estimates. The GPM Profiling Algorithm (GPROF) converts microwave radiation measured by passive microwave (PMW) sensors onboard constellation satellites into precipitation. Over land, precipitation estimates are obtained from high frequency PMW-channels that measure the radiance scattered by ice particles in rain clouds. However, due to the limited scattering related to shallow and light precipitation, it is challenging to distinguish these signals from background radiation that is naturally emitted from the Earth’s surface.</p><p>Increased understanding of the physical processes during precipitation events can be used to improve PMW-based precipitation retrievals. This study couples overpasses of GPM radiometers over the Netherlands to two dual-polarization radars from the Royal Netherlands Meteorological Institute (KNMI). The Netherlands is an ideal setting for this study due to the availability of high-quality ground-based measurements, the frequent occurrence of shallow events, the absence of ground-clutter related to mountains, and the varying background emission related to its coastal location.</p><p>The coupling of overpasses with ground-based precipitation radars provides the opportunity to relate GPROFs performance to physical characteristics of precipitation events, such as the vertical reflectivity profile and dual-polarization information on the melting layer. Furthermore, simultaneous radiometer estimates and space-based reflectivity profiles from the dual-frequency precipitation radar (DPR) onboard the GPM core satellite are coupled to the ground-based reflectivity profiles for selected case studies. Because the a-priori database implemented in the GPROF algorithm is based on observations from the DPR, the comparison of the reflectivity profiles further unravels discrepancies between GPROF and ground-based estimates.</p>

Development of a consistent soil moisture decadal data record from multiple satellites

2020 ◽  
Author(s):  
Steven Chan
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Microwave Radiometer ◽  
Microwave Remote Sensing ◽  
Precipitation Measurement ◽  
X Band ◽  
Data Record ◽  
System Data ◽  
Global Precipitation Measurement ◽  
Global Precipitation

<p>In recent decades, passive microwave remote sensing at lower frequencies (1-10 GHz) has become a primary means to routinely monitor soil moisture on a global scale. Despite the success of a number of L- and C/X-band radiometers independently developed and launched by various government agencies over the last two decades, there has not been a concerted effort to leverage the combined brightness temperature (T<sub>B</sub>) observations from these instruments to derive an integrated soil moisture data record within a consistent geophysical inversion framework. The availability of such a consistent data record would provide critical insights into the dynamics of surface hydrological processes, including anomaly detection, interannual variability, and monitoring of the onset and evolution of long-term spatial and temporal variability due to natural or anthropogenic changes in land surface conditions.</p><p>Recent advances in T<sub>B</sub> intercalibration on current and historical satellites have resulted in the availability of consistent T<sub>B</sub> observations that extend from years to decades. For passive microwave remote sensing of soil moisture, satellite intercalibration undertaken by the Global Precipitation Measurement (GPM) mission [1-2] has resulted in a decadal repository of intercalibrated T<sub>B</sub> observations at X-band (10.7 GHz) frequencies from GMI (2014-present), AMSR2 (2012-present), WindSat (2003-present), TMI (1997-2015) and AMSR-E (2002-2011). Likewise, recent studies on relative calibration by SMOS (2009-present) and SMAP (2015-present) teams have also enabled the production of a similar repository of intercalibrated T<sub>B</sub> observations for soil moisture estimation at L-band (1.41 GHz) frequencies [3]. When used as inputs to a common geophysical inversion model, these T<sub>B</sub> observations can be used for soil moisture estimation. Because consistency has been reinforced at the level of T<sub>B</sub> observations among satellites, the resulting record of soil moisture retrieval is expected to exhibit the same internal consistency. Together, therefore, these T<sub>B</sub> repositories provide the foundation for the development of current and historical consistent soil moisture data records with more frequent and wider coverage than any single satellite can achieve alone.</p><p>In this presentation, we will describe a NASA-funded initiative [4] (MEaSUREs: Making Earth System Data Records for use in Research Environments) to create a consistent soil moisture decadal data record from multiple satellites for terrestrial hydrological applications. Preliminary results, ancillary data preparation, product delivery schedule, and deliverables of this initiative will be discussed in this presentation.</p><p>References:</p><ol><li>Berg, W., S. Bilanow, R. Chen, S. Datta, D. Draper, H. Ebrahimi, S. Farrar, W. Jones, R. Kroodsma, D. McKague, V. Payne, J. Wang, T. Wilheit, and J. Yang. 2016. “Intercalibration of the GPM Microwave Radiometer Constellation,” J. Atmos. Oceanic Technol., 33, pp. 2639–2654, doi: 10.1175/JTECH-D-16-0100.1.</li> <li>Biswas, S. K., S. Farrar, K. Gopalan, A. Santos-Garcia, W. L. Jones and S. Bilanow. 2013. “Intercalibration of Microwave Radiometer Brightness Temperatures for the Global Precipitation Measurement Mission,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 3, pp. 1465–1477. doi: 10.1109/TGRS.2012.2217148.</li> <li>Bindlish, R., S. Chan, T. Jackson, A. Colliander, and Y. Kerr. 2018. “Integration of SMAP and SMOS Observations,” 2018 IEEE IGARSS, Valencia, Spain.</li> <li>"MEaSUREs: Making Earth System Data Records for Use in Research Environments," Accessed Nov 8, 2018. [Online]. Available: https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects</li> </ol>

Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

2014 ◽  
Vol 31 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Yong-Keun Lee ◽  
Zhenglong Li ◽  
Jun Li ◽  
Timothy J. Schmit
Keyword(s):  
Microwave Radiometer ◽  
Precipitable Water ◽  
Retrieval Algorithm ◽  
Earth Observing System ◽  
Radiosonde Observation ◽  
Global Positioning ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Profile ◽  
Infrared Radiances ◽  
Environmental Prediction

Abstract A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer–measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning system–integrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation.

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.

scholarly journals Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

2020 ◽  
Author(s):  
Lisa Milani ◽  
Mark S. Kulie ◽  
Daniele Casella ◽  
Pierre E. Kirstetter ◽  
Giulia Panegrossi ◽  
...  
Keyword(s):  
Ad Hoc ◽  
A Priori ◽  
Precipitable Water ◽  
The United States ◽  
Lake Effect ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Microwave Imager ◽  
Quantitative Precipitation Estimates ◽  
Global Precipitation

AbstractThis study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the United States lower Great Lakes region. GPM Microwave Imager (GMI) high frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard PROfiling (GPROF) QPE retrievals produce inconsistent results when compared against the Multi-Radar/Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not conform with MRMS observations. Ad-hoc precipitation rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-meter temperature, total precipitable water, and background surface type) used to constrain the GPROF a-priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using snow cover a-priori database in the locations of originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a-priori databases to improve intense lake-effect snow detection and retrieval performance.

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>

Improving Precipitation Retrieval by Brightness Temperature Temporal Variation (ΔTB): Definition, Computation, and Application

2021 ◽  
Author(s):  
Yalei You ◽  
Christa Peters-Lidard ◽  
Stephen Munchak ◽  
Sarah Ringerud
Keyword(s):  
Temporal Variation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Daily Rainfall ◽  
Surface Emissivity ◽  
Time Dimension ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Global Precipitation

<p>Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) from a single satellite to estimate the precipitation rate. This study proposed to add the time-dimension into the precipitation estimation process by using the TB (or emissivity) temporal variation (ΔTB or Δe) derived from the Global Precipitation Measurement (GPM) microwave radiometer constellation.  Results showed that (1) ΔTB can improve the precipitation estimation over the cold surfaces (i.e., snow-covered region) through minimizing the microwave land surface emissivity’s influence; (2) Δe under the clear-sky conditions can accurately estimate the daily rainfall accumulation; and (3) ΔTB can be used to identify the liquid raindrop signature over the low surface emissivity areas. This study highlights the importance of maintaining the current passive microwave satellite constellation.</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.

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