Search Results

You are looking at 1 - 10 of 17 items for :

  • Microwave observations x
  • Journal of Hydrometeorology x
  • Global Precipitation Measurement (GPM): Science and Applications x
  • Refine by Access: All Content x
Clear All
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

to the five different 10° latitude bins indicated in the legend. The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval ( Bennartz and Bauer 2003 ; Skofronick-Jackson et al. 2004 , 2019 ; Noh et al. 2009 ; Levizzani et al. 2011 ; Kongoli and Helfrich 2015 ; Chen et al. 2016

Open access
Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

1. Introduction The Global Precipitation Measurement Core Observatory (GPM CO ) satellite, launched in February 2014, offers unprecedented spaceborne observations of the three-dimensional structure of precipitating systems ( Hou et al. 2014 ). The satellite detects rain rates in the range 0.2–110.0 mm h −1 and travels in a sun-asynchronous orbit, providing coverage between 68°N and 68°S, thus augmenting the 37°N/S coverage of the predecessor Tropical Rainfall Measuring Mission (TRMM

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

-changing climate. Despite a long, albeit sparse, record [first known observations date back 2000 BCE ( Wang and Zhang 1988 )], globally complete precipitation measurements did not become available until the modern era of satellite Earth-observing systems that employ infrared and microwave radiometric techniques (e.g., Atlas and Thiele 1981 ). Achieving measurement standards of rainfall in atypical (i.e., extreme) environments on small spatiotemporal scales across the globe, however, has turned out to be more

Full access
Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

1980 ; Hallikainen et al. 1986 , 1987 ). Hence, snow cover has a time-varying effect on snowfall upwelling signal. Physical and empirical approaches have been developed for microwave retrievals of snowfall. Skofronick-Jackson et al. (2004) presented a physical method to retrieve snowfall during a blizzard over the eastern United States using high-frequency observations from the Advanced Microwave Sounding Unit B (AMSU-B) instrument. Kim et al. (2008) simulated atmospheric profiles of a

Full access
Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud

Open access
Linda Bogerd, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet

estimates from various GPM partner satellites with passive microwave (PMW) sensors on board are combined. Additionally, a morphing algorithm is applied to fill time gaps between PMW observations using motion vectors. If the time gap between two subsequent PMW observations is larger than ~30 min, infrared (IR) observations are additionally included to update the final precipitation estimates ( Huffman et al. 2019 , 2020 ). The key difference between IMERG V06B and its previous versions is a modification

Restricted access
Jackson Tan, George J. Huffman, David T. Bolvin, Eric J. Nelkin, and Manikandan Rajagopal


A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN, which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

Restricted access
Manikandan Rajagopal, Edward Zipser, George Huffman, James Russell, and Jackson Tan


The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (IMERG) is a global precipitation product that uses precipitation retrievals from the virtual constellation of satellites with passive microwave (PMW) sensors, as available. In the absence of PMW observations, IMERG uses a Kalman filter scheme to morph precipitation from one PMW observation to the next. In this study, an analysis of convective systems observed during the Convective Process Experiment (CPEX) suggests that IMERG precipitation depends more strongly on the availability of PMW observations than previously suspected. Following this evidence, we explore systematic biases in IMERG through bulk statistics.

In two CPEX case studies, cloud photographs, pilot’s radar, and infrared imagery suggest that IMERG represents the spatial extent of precipitation relatively well when there is a PMW observation but sometimes produces spurious precipitation areas in the absence of PMW observations. Also, considering an observed convective system as a precipitation object in IMERG, the maximum rain rate peaked during PMW overpasses, with lower values between them. Bulk statistics reveal that these biases occur throughout IMERG Version 06. We find that locations and times without PMW observations have a higher frequency of light precipitation rates and a lower frequency of heavy precipitation rates due to retrieval artifacts. These results reveal deficiencies in the IMERG Kalman Filter scheme, which have led to the development of the Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood (SHARPEN; described in a companion paper) that will be applied in the next version of IMERG.

Restricted access
Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

) has been used by several retrieval algorithms ( You et al. 2015 ; Kummerow et al. 2015 ). To largely avoid the possible surface contamination, instead of using the signatures from window channels (e.g., 85 GHz), Staelin and Chen (2000) developed a rainfall retrieval algorithm solely dependent on the microwave observations near opaque water vapor and oxygen absorption channels (183 and 52 GHz). Brocca et al. (2014) proposed a conceptually different rainfall retrieval algorithm by using the soil

Restricted access
Jackson Tan, Walter A. Petersen, and Ali Tokay

-top temperatures. Much progress has been made in the last two decades with a contingent of low-Earth-orbiting passive microwave satellites and two NASA/JAXA spaceborne radars in the microwave band, the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. Unlike infrared radiation, microwave radiation is able to penetrate clouds and interact more directly with precipitation; consequently, microwave retrieval techniques generally provide a superior estimate of

Full access