Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG)

The climate and weather forecast predictive capability for precipitation intensity is limited by gaps in the understanding of basic cloud-convective processes. Currently, a better understanding of the cloud-convective process lacks observational constraints, due to the difficulty in obtaining accurate, vertically resolved pressure, temperature, and water vapor structure inside and near convective clouds. This manuscript describes the potential advantages of collecting sequential radio occultation (RO) observations from a constellation of closely spaced low Earth-orbiting satellites. In this configuration, the RO tangent points tend to cluster together, such that successive RO ray paths are sampling independent air mass quantities as the ray paths lie "parallel" to one another. When the RO train orbits near a region of precipitation, there is a probability that one or more of the RO ray paths will intersect the region of heavy precipitation, and one or more would lie outside. The presence of heavy precipitation can be discerned by the use of the polarimetric RO (PRO) technique recently demonstrated by the Radio Occultations through Heavy Precipitation (ROHP) receiver onboard the Spanish PAZ spacecraft. This sampling strategy provides unique, near-simultaneous observations of the water vapor profile inside and in the environment surrounding heavy precipitation, which are not possible from current RO data. temperature vertical and horizontal structures in the surrounding region. Much of the water vapor resides in the 2 km nearest the Earth, the approximate top of the boundary layer, which is the layer where air motions are directly influenced by contact with the Earth's surface. However, increasing evidence points to the control of deep convection by the relatively smaller and more variable amount of moisture above the boundary layer, known as the free troposphere. When abundant, free tropospheric moisture can strengthen convection by making the atmosphere more unstable. Horizontal transport and the mixing of nearby dry air (through a process known as entrainment) can weaken the convection, by decreasing the buoyancy [2,3]. Free-tropospheric moisture thus appears to exert significant control on the development of deep convection, producing heavy precipitation [4][5][6][7][8][9][10][11].Currently, a better understanding of the cloud-convective process lacks observational constraints, due to the difficulty in obtaining accurate, vertically resolved pressure, temperature, and water vapor (denoted by p, T, and q, respectively) structure in and near convection. Infrared sensors, such as the Atmospheric Infrared Sounder (AIRS), provide water vapor structure outside of clouds and precipitation, but cannot estimate the water vapor inside clouds. Passive microwave (MW) sounding radiometers, such as the Advanced Technology Microwave Sounder (ATMS), operate in millimeter-wave (183 GHz) water vapor bands, which under heavy precipitation, are sensitive to the cloud top ice region [12], limiting their use in studies that require ...

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benefits of a Closely-Spaced Satellite Constellation of Atmospheric Polarimetric Radio Occultation Measurements

Turk

Padullés

et al. 2019

“…For estimating the changes of σ 0 due to precipitation, global precipitation measurement (GPM) data were used, namely integrated multi-satellite retrievals for GPM (IMERG) with a 30 min period of precipitation accumulation [30]. The interval of observations were 27 April 2018-November 2018.…”

Section: Satellite Datamentioning

confidence: 99%

Monitoring Harvesting by Time Series of Sentinel-1 SAR Data

Kavats¹,

Khramov²,

Sergieieva³

et al. 2019

Algorithm for determining crop harvesting dates based on time series of coherence and backscattering coefficient (σ 0 ) derived from Sentinel-1 single look complex (SLC) synthetic-aperture radar (SAR) images is proposed. The algorithm allows the ability to monitor harvesting over large areas without having to install additional sensors on agricultural machinery. Coherence between SAR images allows the ability to track changes in field-scatterers configuration resulting from agricultural work. The proposed algorithm finds a step-like increase in coherence that occurs after the harvesting and is related to the conversion of a field into a bare soil area. An additional check of potential harvest dates is carried out by threshold values of σ 0 depending on vegetation height. The algorithm is adapted for the monitoring of non-homogeneous fields with traces of erosion and insertions of fallow land. The algorithm was tested on agricultural fields located in the north of Kazakhstan. The obtained accuracy (mean absolute error = 6.5 days) of determining the dates of harvesting can be deemed satisfactory. This accuracy can be increased by shortening the interval between observations from 12 to 6 days when using data from both Sentinel-1 satellites.

“…As IMERG products are provided at 30 min temporal resolution, MW retrievals are obtained either from calibrated conical-scan microwave radiometer or cross-track-scan microwave retrieval, essentially from the one that is closer to the half-hour window. IR estimates are provided by various sensors (refer to [21] for more details). The IMERG algorithm is run twice in near-real time and once after monthly gauge data become available to generate the following multi-satellite products: (i) early, available~4 h after the observation time allowing quick assessment for flood and landslide forecasts; (ii) late, available~12 h after the observation time, mainly for agricultural applications like drought monitoring, crop yield forecasts, and crop production; and (iii) final, availablẽ 2 months after observation for research applications.…”

Section: Datamentioning

confidence: 99%

Assessment of Level-3 Gridded Global Precipitation Mission (GPM) Products Over Oceans

Khan¹,

Maggioni²

2019

The performance of Level-3 gridded Global Precipitation Mission (GPM)-based precipitation products (IMERG, Integrated Multi-satellite Retrievals for GPM) is assessed against two references over oceans: the OceanRAIN dataset, derived from oceanic shipboard disdrometers, and a satellite-based radar product (the Level-3 Dual-frequency Precipitation Radar, 3DPRD). Daily IMERG products (early, late, final) and microwave-only (MW) and Infrared-only (IR) precipitation components are evaluated at four different spatial resolutions (0.5 • , 1 • , 2 • , and 3 • ) during a 3-year study period (March 2014-February 2017). Their performance is assessed based on both categorical and continuous performance metrics, including correlation coefficient, probability of detection, success ratio, bias, and root mean square error (RMSE). A triple collocation analysis (TCA) is also presented to further investigate the performance of these satellite-based products. Overall, the IMERG products show an underestimation with respect to OceanRAIN. Rain events in OceanRAIN are correctly detected by all IMERG products~80% of the times. IR estimates show relatively large errors and low correlations with OceanRAIN compared to the other products. On the other hand, the MW component performs better than other products in terms of both categorical and continuous statistics. TCA reveals that 3DPRD performs consistently better than OceanRAIN in terms of RMSE and coefficient of determination at all spatial resolutions. This work is part of a larger effort to validate GPM products over nontraditional regions such as oceans.