The Global Hydro-Estimator (GHE) Product: The GHE extends the current operational GOES rainfall estimate capability from only over the continental U.S. to the entire globe equator-ward of 60 degrees to meet users' need in supporting global flash flood guidance. The Hydro-Estimator (H-E) is a single-channel (11-μm) rain rate algorithm whose origins go back to the Auto-Estimator (A-E; Vicente et al. 1998) algorithm. The primary feature of the A-E is a fixed relationship between rainfall rate and that was derived from 6800 pairs of collocated IR brightness temperatures and radar rainfall rates from convective cores of mesoscale convective systems (MCSs) for 16 events from March-June 1995. The improved algorithm differentiates between "convective core" and "non-core" precipitation (based on empirical rather than physical definitions) and assigns a rain rate that is a combination of the two depending on the spatial characteristics of the nearby cloud mass. Both the presence/absence of precipitation and its intensity are a function of the extent to which a particular pixel is "core" or "non- core". The H-E assigns rainfall only to pixels that are colder than the average of the surrounding cloudy pixels in order to eliminate cirrus clouds. Numerical Weather Prediction (NWP) outputs are also being incorporated in this algorithms. Correction factors range from orographic driven precipitation, where a Digital Elevation Model (DEM) and low tropospheric winds are used to adjust the final retrievals to humidity factors (relative humidity and precipitable water) to decrease rainfall rates in very dry environments and increases them in very moist ones.

The operational GHE product includes 1 hour accumulation from 60 degrees north to 60 degrees south is displayed in order to identify the areas with current severe weather (high rain rates) and monitoring the evolution of those storms. In this case, the latency is dramatically reduced due to this algorithm relies only in GEO-IR data.

Detailed description about the Hydroestimator Algorithm can be found in: NOAA/OSPO – http://www.ospo.noaa.gov/Products/atmosphere/ghe/algo.html.

Forecasting and Tracking of Active Cloud Clusters (ForTrACC) product: This short-term forecast algorithm is implemented at CPTEC/INPE since 2004 for tracking Mesoscale Convective Systems and it is fully described in Vila et al 1998. For SCOPE-Nowcasting Pilot Project, the original algorithm was adapted to ingest GHE data instead of GEO-IR brightness temperature while the output produces 60, 120 and 180 minutes 1-hour accumulation rainfall forecast considering the GHE estimated rainfall features as the initial condition (not necessarily the ground truth). To avoid confusions between tracking clouds or rainfall features, this new implementation will be called HydroTrack.

The methodology used by HydroTrack is based, similar to ForTrACC technique, in three steps. i) Detection of rainfall features: To capture a spectrum of storm types, we used a single rainfall threshold of 0.1 mm and a size threshold of 100 pixels. This rain rate represents light rain events. The size threshold reduces the number of tracked storms by filtering out small-scale events and reducing the number of splits and merges. ii) Tracking using area overlap: HydroTrack uses an area overlap technique to track the storms, both forward and backward in time. If two cloud clusters identified in different time steps have shared pixels, they were considered the same system and assigned a family number. If more than one match was found, the largest overlap system was tracked. iii) Forecast: the near-term forecast (1 hour) is based on the extrapolation of the current observed system along its life cycle and a statistical approach of rainfall features life cycle (initiation, mature stage and dissipation) to predict that stage for the next hour. This process is repeated three times to produce up to 3-hours forecast of current rainfall features.

Detailed description about the Fortracc algorithm can be found in the following URL: http://sigma.cptec.inpe.br/fortracc/?i=en.

The Integrated Multi-satellitE Retrievals for GPM (IMERG) -real time version- product: This dataset is the unified U.S. algorithm that provides the Day-1 multi-satellite precipitation product for the U.S. GPM team. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2014 version of the Goddard Profiling Algorithm (GPROF2014), then gridded, intercalibrated to the GPM Combined Instrument product, and combined into half-hourly 0.1°x0.1° fields. These are provided to both the Climate Prediction Center (CPC) Morphing-Kalman Filter (CMORPH-KF) Lagrangian time interpolation scheme and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated "even-odd" geo-IR fields and forward them to PPS for use in the CMORPH-KF Lagrangian time interpolation scheme and the PERSIANN-CCS computation routines. The PERSIANN-CCS estimates are computed (supported by an asynchronous re-calibration cycle) and sent to the CMORPH-KF Lagrangian time interpolation scheme. The CMORPH-KF Lagrangian time interpolation (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. The IMERG system is run twice in near-real time.

Specific information about this dataset can be accessed thought the following URL: http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.4.pdf.

References:

Huffman, George J., et al. "The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales." Journal of Hydrometeorology 8.1 (2007): 38-55.

Vicente, Gilberto A., Roderick A. Scofield, and W. Paul Menzel. "The operational GOES infrared rainfall estimation technique." Bulletin of the American Meteorological Society 79.9 (1998): 1883-1898.

Vila, Daniel A., et al. "Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) using satellite infrared imagery: Methodology and validation." Weather and Forecasting 23.2 (2008): 233-245.