Browse

You are looking at 31 - 40 of 48 items for :

  • Global Precipitation Measurement (GPM): Science and Applications x
  • All content x
Clear All
E. F. Stocker, F. Alquaied, S. Bilanow, Y. Ji, and L. Jones

Abstract

The National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F). The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent.

A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products.

Full access
Lijing Cheng, Hao Luo, Timothy Boyer, Rebecca Cowley, John Abraham, Viktor Gouretski, Franco Reseghetti, and Jiang Zhu

Abstract

Biases have been identified in historical expendable bathythermograph (XBT) datasets, which are one of the major sources of uncertainty in the ocean subsurface database. More than 10 correction schemes were proposed; however, their performance has not been collectively evaluated and compared. This study quantifies how well 10 different available schemes can correct the historical XBT data by comparing the corrected XBT data with collocated reference data in both the World Ocean Database (WOD) 2013 and the EN4 dataset. Four different metrics are proposed to quantify their performances. The results indicate CH14 is the best among the currently available methods, and L09/G12/GR10 can be used with some caveats. To test the robustness of the schemes, we further train the CH14 and L09 by using 50% of the XBT–reference data and the schemes are tested by using the remaining data. The results indicate that the two schemes are robust. Moreover, the EN4 and WOD comparison datasets show a systematic difference of XBT error (~0.01°C on a global scale and 0–700 m on average). influences of quality control and data processing have been investigated. Additionally, the side-by-side XBT–CTD comparison experiment is used to examine the correction schemes and provides independent high-quality data for the assessment. The schemes that best correct the global datasets do not always perform as well at correcting the side-by-side dataset, and further examination of the discrepancy in performance is still required. Finally, CH14 and L09 result in very similar ocean heat content (OHC) change estimates in the upper 700 m since 1966, suggesting the potential of reducing XBT-induced error in OHC estimates.

Open access
M. Petracca, L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca

Abstract

The Ka–Ku Dual-Frequency Precipitation Radar (DPR) and the Microwave Imager on board the Global Precipitation Measurement (GPM) mission core satellite have been collecting data for more than 3 years, providing precipitation products over the globe, including oceans and remote areas where ground-based precipitation measurements are not available. The main objective of this work is to validate the GPM-DPR products over a key climatic region with complex orography such as the Italian territory. The performances of the DPR precipitation rate products are evaluated over an 18-month period (July 2015–December 2016) using both radar and rain gauge data. The ground reference network is composed of 22 weather radars and more than 3000 rain gauges. DPR dual-frequency products generally show better performance with respect to the single-frequency (i.e., Ka- or Ku-band only) products, especially when ground radar data are taken as reference. A sensitivity analysis with respect to season and rainfall intensity is also carried out. It was found that the normal scan (NS) product outperforms the high-sensitivity scan (HS) and matched scan (MS) during the summer season. A deeper analysis is carried out to investigate the larger discrepancies between the DPR-NS product and ground reference data. The most relevant improvement of the DPR products’ performance was found by limiting the comparison to the upscaled radar data with a higher quality index. The resulting scores in comparison with ground radars are mean error (ME) = −0.44 mm h−1, RMSE = 3.57 mm h−1, and fractional standard error (FSE) = 142%, with the POD = 65% and FAR = 1% for rainfall above 0.5 mm h−1.

Full access
Catherine M. Naud, James F. Booth, Matthew Lebsock, and Mircea Grecu

Abstract

Using 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.

Full access
Toshio Iguchi, Nozomi Kawamoto, and Riko Oki

Abstract

Detection of ice precipitation is one of the objectives in the Global Precipitation Measurement (GPM) mission. The dual-frequency precipitation radar (DPR) can provide precipitation echoes at two different frequencies, which may enable differentiating solid precipitation echoes from liquid precipitation echoes. A simple algorithm that flags the pixels that contain intense ice precipitation above the height of C is implemented in version 5 of the DPR products. In the inner swath of DPR measurements in which both Ku- and Ka-band radar echoes are available, the measured dual-frequency ratio () together with the measured radar reflectivity factor is used to judge the existence of intense ice precipitation. Comparisons of the flagged pixels with surface measurements show that the algorithm correctly identifies relatively intense ice precipitation regions. The global distribution of the flagged pixels indicates an interesting difference between land and ocean, in particular in the distribution of ice precipitation that reaches the surface. The flag is also expected to be useful for improving precipitation retrieval algorithms by microwave radiometers.

Open access
Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

Abstract

Prominent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high-frequency (e.g., 89 GHz) brightness temperature depression (i.e., the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g., −30% over the United States) observed during extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution, and aerosol concentrations to form a stronger relationship between precipitation and the scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use the large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduces the difference between the assumed and observed variability in the ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the United States, the results demonstrate outstanding potential in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20%–30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm.

Full access
Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

Abstract

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

Full access
Ali Tokay, Leo Pio D’Adderio, Federico Porcù, David B. Wolff, and Walter A. Petersen

Abstract

A network of seven two-dimensional video disdrometers (2DVD), which were operated during the Midlatitude Continental Convective Clouds Experiment (MC3E) in northern Oklahoma, are employed to investigate the spatial variability of raindrop size distribution (DSD) within the footprint of the dual-frequency precipitation radar (DPR) on board the National Aeronautics and Space Administration’s Global Precipitation Measurement (GPM) mission core satellite. One-minute 2DVD DSD observations were interpolated uniformly to 13 points distributed within a nearly circular DPR footprint through an inverse distance weighting method. The presence of deep continental showers was a unique feature of the dataset resulting in a higher mean rain rate R with respect to previous studies. As a measure of spatial variability for the interpolated data, a three-parameter exponential function was applied to paired correlations of three parameters of normalized gamma DSD, R, reflectivity, and attenuation at Ka- and Ku-band frequencies of DPR (Z_Ka, Z_Ku, k_Ka, and k_Ku, respectively). The symmetry of the interpolated sites allowed quantifying the directional differences in correlations at the same distance. The correlation distances d 0 of R, k_Ka, and k_Ku were approximately 10 km and were not sensitive to the choice of four rain thresholds used in this study. The d 0 of Z_Ku, on the other hand, ranged from 29 to 20 km between different rain thresholds. The coefficient of variation (CV) remained less than 0.5 for most of the samples for a given physical parameter, but a CV of greater than 1.0 was also observed in noticeable samples, especially for the shape parameter and Z_Ku.

Full access
Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

Abstract

The Olympic Mountains Experiment (OLYMPEX) took place during the 2015/16 fall–winter season in the vicinity of the mountainous Olympic Peninsula of Washington State. The goals of OLYMPEX were to provide physical and hydrologic ground validation for the U.S.–Japan Global Precipitation Measurement (GPM) satellite mission and, more specifically, to study how precipitation in Pacific frontal systems is modified by passage over coastal mountains. Four transportable scanning dual-polarization Doppler radars of various wavelengths were installed. Surface stations were placed at various altitudes to measure precipitation rates, particle size distributions, and fall velocities. Autonomous recording cameras monitored and recorded snow accumulation. Four research aircraft supplied by NASA investigated precipitation processes and snow cover, and supplemental rawinsondes and dropsondes were deployed during precipitation events. Numerous Pacific frontal systems were sampled, including several reaching “atmospheric river” status, warm- and cold-frontal systems, and postfrontal convection.

Open access
Xiang Ni, Chuntao Liu, Daniel J. Cecil, and Qinghong Zhang

Abstract

In previous studies, remote sensing properties of hailstorms have been discussed using various spaceborne sensors. Relationships between hail occurrence and strong passive microwave brightness temperature depressions have been established. Using a 16-yr precipitation-feature database derived from the Tropical Rainfall Measuring Mission (TRMM) satellite, the performance of the TRMM Precipitation Radar and TRMM Microwave Imager is further investigated for hail detection. Detection criteria for hail larger than 19 mm are separately developed from Ku-band radar reflectivity and microwave brightness temperature properties of precipitation features that are collocated with surface hail reports over the southeastern and south-central United States. A threshold of 44 dBZ at −22°C is found to have the highest critical success index and Heidke skill score. The threshold of 230 K at 37 GHz yields the best scores among passive microwave properties. Using these two thresholds, global distributions of possible hail events are generated over 65°S–65°N using two years of observations from the Global Precipitation Measurement Core Observatory satellite. Differences in the derived hail geographical distributions are found between radar and passive microwave methods over tropical South America, the “Maritime Continent,” west-central Africa, Argentina, and South Africa. These discrepancies result from different vertical structures of the maximum radar reflectivity profiles over these regions relative to the southeastern and south-central United States, where the thresholds were established. Those differences generally led to overestimates in the tropics from the passive microwave methods, relative to the radar-based methods.

Open access