What we can learn from the satellite-based rainfall footprint of Superstorm Sandy: a preliminary synopsis

Image of a TRMM Multisatellite Precipitation Analysis of cumulative precipitation (mm) for the period of Oct. 24-31, 2012, with superimposed track. Image Credit: Harold Pierce, NASA/GSFC.

Figure 1: TRMM Multisatellite Precipitation Analysis of cumulative precipitation (mm) for the period of Oct. 24-31, 2012, with superimposed track. Image Credit: Harold Pierce, NASA/GSFC.

J. Marshall Shepherd
University of Georgia, Athens

1. Introduction

Superstorm Sandy was one of the deadliest and most costly storms of the 21st century so far. By Nov. 8, 2012, the death toll had reach nearly 200 people in the Caribbean and United States. Economists estimate that infrastructure, insurance claims, and other costs will likely be in the $50-100 billion range from Sandy alone. Clearly, storms like Sandy are significant socio-economic events. A recent study by Lazo et al (2009) notes that weather impacts roughly 3.4 percent of the entire U.S. Gross Domestic Product (GDP), based on estimates through 2008 — roughly an economic cost of $485 billion. Given the impact of Sandy and other weather disasters from 2008 to 2012, this number is likely an underestimate.

Our society has little experience with a Sandy-type storm catching citizens by surprise like the deadly Galveston storm of 1900, which killed thousands. From the first weather satellite (TIROS-1) to the current GOES and NOAA/Suomi NPP satellites, we now use an array of remote sensing technology to monitor the tropical environment. Recently, a new generation of satellite capabilities has emerged as a credible method for measuring precipitation. This is particularly valuable for monitoring hurricanes, which are primarily ocean-based weather systems.

Rappaport [2000] noted that inland freshwater flooding was responsible for more than half of the 600 deaths in Atlantic tropical cyclones (1970–1999) in the United States. Rainfall from Sandy was not the dominant cause of flooding in coastal New Jersey and New York City, but it was a contributor. Rainfall-related flooding was more significant in regions west and south of the landfall region. However, quantification of rainfall in tropical cyclones offers the potential for improvements in intensity forecasts, which currently lag behind track forecasting, and flood potential (Kelley and Halverson 2011; Shepherd et al. 2007).

Figure showing sea surface temperature (°C) with ~26-degree isotherm (dash line) on Oct. 27, 2012. Image Credit: NOAA/NCEP/EMC/MMAB/RTOFS.

Figure 2: Sea surface temperature (°C) with ~26-degree isotherm (dash line) on Oct. 27, 2012. Image Credit: NOAA/NCEP/EMC/MMAB/RTOFS.

Objectives and Data

There are many interesting things to be learned from this preliminary examination of Sandy’s “rainfall footprint.” Herein, the objectives are to: (1) provide a preliminary synopsis of the lifecycle of Superstorm Sandy from the perspective of satellite-derived rainfall, (2) examine internal storm structure characteristics, and (3) explore whether documented patterns in rainfall associated with tropical-to-extra-tropical storm transition were observed.
Satellite-based daily rainfall amounts from NASA’s Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) from Oct. 24-31, 2012, were processed by NASA’s Goddard Space Flight Center. TMPA is a 3-hourly, 0.25-degree (~25 km) product described in Huffman et al. (2007).

This study employs the merged TMPA_3B42 3-Hourly-Real Time product, which is not calibrated by gauges like the research version of the product. TMPA is composed of available microwave observations (e.g., TRMM microwave imager, Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer (AMSR) and Advanced Microwave sounding Unit (AMSU)) and calibrated infrared (IR) estimates. In 2012, version 7 of the TMPA algorithms became available. The major changes between version 6 and 7 are described on the NASA Precipitation Processing System website and include: (1) the inclusion of next generation Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager Sounder (SSMIS) within the suite of microwave products, (2) an improved Goddard Profiling Algorithm (GPROF2010), (3) the latest Advanced Microwave Sounding Unit and Microwave Humidity Sounder retrieval algorithms, and (4) the Version 6 Global Precipitation Climatology Centre precipitation gauge observations.


Sandy, at U.S. landfall, was a unique storm that represented the convergence of three weather systems: a late season hurricane, an early season mid-latitude cyclone or nor’easter, and a persistent anticyclone over Greenland. However, there were many interesting aspects of the storm prior to U.S. landfall.

Figure showing TRMM Precipitation Radar backscatter at 2:20 EDT on Oct. 28, 2012. Image Credit: NASA.

Figure 3: TRMM Precipitation Radar backscatter at 2:20 EDT on Oct. 28, 2012. Image Credit: NASA.

Hurricane Sandy had its origins in the southern Caribbean Sea. Early in its lifecycle (i.e. Oct. 21-24), Sandy produced large rainfall totals (> 300 mm) over eastern Jamaica. Sandy made landfall in southeastern Jamaica as a Category 1 storm. Heavy rains coupled with complex orography led to significant flooding and landslides. Sandy made landfall in eastern Cuba as a Category 2. Though likely weakened over Cuba, Sandy still caused flooding and landslides in the mountainous terrain. The rainfall footprint (Figure 1) illustrates sporadic periods of “rainfall intensification” as the storm moved into the Bahamas. Figure 2 illustrates that “bursts” of intensification may have been drawing on isolated pockets of warm water, particularly just north of the Bahamas. The storm weakened to a Category 1 storm as it moved north of the Bahamas. Figure 2 suggests that cooler water was inhibiting development, as was stronger mid-latitude shear. The TRMM Precipitation Radar (Figure 3) found evidence of a fairly well-defined and compact eyewall, generally characteristic of stronger Category storms. Rainfall also is a proxy for latent heat energy released as hurricanes transfer energy from the ocean to the atmosphere. Kelley and Halverson (2011) found that convective bursts in tropical cyclones may release in 12 h an extra 6×1017 J of latent heat, comparable to humanity’s total energy consumption in the same time period. They linked this energy release to kinetic energy, tangential wind speed increases, and intensification.

Another significant “rainfall intensification” period (Figure 1) was observed just east of North Carolina and Virginia/Maryland coastal regions. It is clear from Figure 2 that Sandy was feeding on very warm waters associated with the Gulf Stream. Some aircraft reconnaissance reports found barometric pressure and wind readings supporting this intensification period. As the storm transitioned from a tropical system, deriving its energy from the ocean, to a mid-latitude cyclone, deriving its energy from the atmosphere, it was able to produce more than 220 mm of rainfall during the 24-36 hour period prior to landfall, even though water temperatures were relatively cooler closer to Delaware and New Jersey.

One interesting observation is the leftward shift of the rainfall footprint relative to the track near landfall. Atallah et al. (2007) noted that such left of track distributions are indicative of tropical cyclones undergoing extratropical transition (ET). They argued that this is typically associated with a positively tilted midlatitude trough approaching the storm from the northwest, which shifts the precipitation to the north-northwest of the storm center. It also is evident that the warmest waters were to the left of the storm track.

Image showing rainfall composite (mm/yr) of all 2012 western Atlantic named storms. Image Credit: Harold Pierce, NASA/GSFC.

Figure 4: Rainfall composite (mm/yr) of all 2012 western Atlantic named storms. Image Credit: Harold Pierce, NASA/GSFC.

As the storm moved inland, it still produced more than 100 mm of rainfall, though the heavier amounts were in the Delmarva Peninsula and the interior of Maryland and Pennsylvania. It should be noted that snowfall is likely underestimated with this particular rainfall product, though future capabilities on the Global Precipitation Measurement (GPM) mission should offer improved capabilities in this respect.

Concluding Statements

While the experimental real-time TMPA is not intended for research use, it provides very useful first-order estimates of rainfall in tropical systems. For example, Figure 4 is a summary of the composite rainfall of all named storms in the Atlantic Basis for 2012. The gauge-calibrated version TMPA is suitable for quantitative research and is shedding light on an array of hydrometeorological extremes and associated statistical properties. The TRMM PR (and future GPM dual-frequency radar) will continue to improve our understanding of intensification processes. Already, this preliminary analysis of Sandy’s rainfall footprint is quite revealing from a scientific standpoint.


Atallah, E., L. Bosart, and A. Aiyyer, (2007) Precipitation Distribution Associated with Landfalling Tropical Cyclones over the Eastern United States. Monthly Weather Review, 135, 2185-2206
Huffman, G. J., and Coauthors, (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scale. J. Hydrometeor., 8, 38–55
Kelley, O., and J. Halverson, (2011) How much tropical cyclone intensification can result from the energy released inside of a convective burst? J. of Geophysical Research. Vol. 116, doi:10.1029/2011JD015954
Lazo, J., R. Morss, and J. Demuth, (2009) 300 Billion Served: Sources, Perceptions, Uses, and Values of Weather Forecasts. Bulletin of the AMS, 90,785-798
Rappaport, E. N. (2000) Loss of life in the United States associated with recent Atlantic tropical cyclones. Bulletin of the AMS, 81, 2065–2074
Shepherd, J.M., A. Grundstein, and T.L. Mote, (2007) Quantifying the contribution of tropical cyclones to extreme rainfall along the coastal southeastern United States. Geophysical Research Letters, Vol. 34, doi:10.1029/2007GL031694

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