Mathew R. P. Sapiano

Environmental signatures associated with cholera epidemics

The causative agent of cholera, Vibrio cholerae, has been shown to be autochthonous to riverine, estuarine, and coastal waters along with its host, the copepod, a significant member of the zooplankton community. Temperature, salinity, rainfall and plankton have proven to be important factors in the ecology of V. cholerae, influencing the transmission of the disease in those regions of the world where the human population relies on untreated water as a source of drinking water. In this study, the pattern of cholera outbreaks during 1998–2006 in Kolkata, India, and Matlab, Bangladesh, and the earth observation data were analyzed with the objective of developing a prediction model for cholera. Satellite sensors were used to measure chlorophyll a concentration (CHL) and sea surface temperature (SST). In addition, rainfall data were obtained from both satellite and in situ gauge measurements. From the analyses, a statistically significant relationship between the time series for cholera in Kolkata, India, and CHL and rainfall anomalies was determined. A statistically significant one month lag was observed between CHL anomaly and number of cholera cases in Matlab, Bangladesh. From the results of the study, it is concluded that ocean and climate patterns are useful predictors of cholera epidemics, with the dynamics of endemic cholera being related to climate and/or changes in the aquatic ecosystem. When the ecology of V. cholerae is considered in predictive models, a robust early warning system for cholera in endemic regions of the world can be developed for public health planning and decision making.

An Error Model for Uncertainty Quantification in High-Time-Resolution Precipitation Products

AbstractThis study proposes a new framework, Precipitation Uncertainties for Satellite Hydrology (PUSH), to provide time-varying, global estimates of errors for high-time-resolution, multisatellite precipitation products using a technique calibrated with high-quality validation data. Errors are estimated for the widely used Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product at daily/0.25° resolution, using the NOAA Climate Prediction Center (CPC) Unified gauge dataset as the benchmark. PUSH estimates the probability distribution of reference precipitation given the satellite observation, from which the error can be computed as the difference (or ratio) between the satellite product and the estimated reference. The framework proposes different modeling approaches for each combination of rain and no-rain cases: correct no-precipitation detection (both satellite and gauges measure no precipitation), missed precipitation (satellite records a zero, but the gaug...

Toward an Intercalibrated Fundamental Climate Data Record of the SSM/I Sensors

Multiple independent intercalibration techniques are used to derive calibration adjustments for the development of a fundamental climate data record of physically consistent brightness temperature data from the series of six special sensor microwave/imagers (SSM/Is). The techniques include direct polar matchups, double differencing against model simulations from reanalysis profile data, double differencing against matchups with the Tropical Rainfall Measuring Mission Microwave Imager, vicarious cold calibration, and an Amazon warm calibration. Multiple realizations of three of the five techniques have been applied using different reanalysis data and retrieval techniques to account for Earth incidence angle-dependent differences between sensors. Excellent agreement has been achieved between each of the techniques with typical spread within 0.5 K at the cold end, with slightly higher spread when the warm end estimate is included. A strategy for estimating mean intercalibration values is described with justification for the use of a simple offset based on error characteristics. Intercalibration offsets are smaller for the more recent SSM/I (<; 1 K for F14 and F15 compared with F13) and slightly larger for the older satellites (<; 2 K for F08, F10, and F11 when compared to F13).

The observed sensitivity of the global hydrological cycle to changes in surface temperature

Climate models project large changes in global surface temperature in coming decades that are expected to be accompanied by significant changes in the global hydrological cycle. Validation of model simulations is essential to support their use in decision making, but observing the elements of the hydrological cycle is challenging, and model-independent global data sets exist only for precipitation. We compute the sensitivity of the global hydrological cycle to changes in surface temperature using available global precipitation data sets and compare the results against the sensitivities derived from model simulations of 20th century climate. The implications of the results for the global climate observing system are discussed.

Improved Geolocation and Earth Incidence Angle Information for a Fundamental Climate Data Record of the SSM/I Sensors

The long-term data record of microwave imager data from the series of six Special Sensor Microwave/Imagers (SSM/Is) on board the Defense Meteorological Satellite Program (DMSP) spacecraft has been used to produce global multidecadal time series of a number of geophysical parameters, including precipitation, total precipitable water, ocean surface wind speed, and sea ice extent. As part of an effort to produce an intercalibrated fundamental climate data record (CDR) of the brightness temperature (Tb) data from the SSM/I, an examination of geolocation errors and the subsequent impact on the view angle [or the Earth incidence angle (EIA)] is performed. Using a combination of techniques, estimates of changes in the sensor/spacecraft attitude, including deviations in roll, pitch, and yaw, have been computed for the life of each of the SSM/I sensors. Applying these corrections results in an improved pixel geolocation, but more importantly, it provides accurate estimates of the EIA across the scan and throughout each orbit. An analysis of uncertainties in the calculation of EIA shows mean errors within 0.1 °, which translates to errors in the calibration of less than 0.2 K for all channels. The availability of these precise estimates of EIA is extremely important for producing CDRs since the mean EIA decreases over time due to the decay in the DMSP orbits, which will lead to an artificial climate trend if not properly accounted for by the geophysical retrieval algorithms.

Merged Statistical Analyses of Historical Monthly Precipitation Anomalies Beginning 1900

Abstract A monthly reconstruction of precipitation beginning in 1900 is presented. The reconstruction resolves interannual and longer time scales and spatial scales larger than 5° over both land and oceans. Because of different land and ocean data availability, the reconstruction combines two separate historical reconstructions. One analyzes interannual variations directly by fitting gauge-based anomalies to large-scale spatial modes. This direct reconstruction is used for land anomalies and interannual oceanic anomalies. The other analyzes annual and longer variations indirectly from correlations with analyzed sea surface temperature and sea level pressure. This indirect reconstruction is used for oceanic variations with time scales longer than interannual. In addition, a method of estimating reconstruction errors is also presented. Over land the reconstruction is a filtered representation of the gauge data with data gaps filled. Over oceans the reconstruction gives an estimate of the atmospheric respons...

A New High-Resolution Satellite-Derived Precipitation Dataset for Climate Studies

Abstract The importance of analyzing climate at high spatiotemporal resolution has been emphasized in the recent Intergovernmental Panel on Climate Change (IPCC) report. Several high-resolution analyses of global precipitation have recently been created to meet this need by combining high-quality passive microwave estimates with frequently sampled geosynchronous infrared estimates. A new daily 0.25° analysis has been developed at the Cooperative Institute for Climate Studies (CICS), called the CICS High-Resolution Optimally Interpolated Microwave Precipitation from Satellites (CHOMPS), which is based only on passive microwave satellite estimates. The analysis was developed using all available sensors and the most up-to-date common retrieval scheme. An important advantage of CHOMPS is, therefore, its consistency. The microwave estimates from the different sensors at hourly time scales are combined using optimum interpolation (OI), using estimates of the noise and spatial correlation scales and standardized...

Estimating Uncertainties in High-Resolution Satellite Precipitation Products: Systematic or Random Error?

AbstractThis study proposes a method to quantify systematic and random components of the error associated with satellite precipitation products. Specifically, the Precipitation Uncertainties for Satellite Hydrology (PUSH) model is expanded to provide an estimate of those components of the root-mean-square error. The framework is tested on the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42, real time (3B42RT), and 3B42, version 7 (3B42V7), products over the contiguous United States, using the NOAA Climate Prediction Center (CPC) Unified gauge product as reference. Results show that 3B42V7 exhibits much smaller errors than the real-time product and that the major component of the error associated with both TMPA 3B42 products is random, as the systematic error is almost completely removed by the bias adjustment applied to the two products. A strong dependence of both systematic and random error components on satellite rain rates—with larger error components at larger rain rates—is observed for both s...

Diagnosis of Variability and Trends in a Global Precipitation Dataset Using a Physically Motivated Statistical Model

Abstract A physically motivated statistical model is used to diagnose variability and trends in wintertime (October–March) Global Precipitation Climatology Project (GPCP) pentad (5-day mean) precipitation. Quasigeostrophic theory suggests that extratropical precipitation amounts should depend multiplicatively on the pressure gradient, saturation specific humidity, and the meridional temperature gradient. This physical insight has been used to guide the development of a suitable statistical model for precipitation using a mixture of generalized linear models: a logistic model for the binary occurrence of precipitation and a Gamma distribution model for the wet day precipitation amount. The statistical model allows for the investigation of the role of each factor in determining variations and long-term trends. Saturation specific humidity qs has a generally negative effect on global precipitation occurrence and with the tropical wet pentad precipitation amount, but has a positive relationship with the penta...

A Consensus Calibration based on TMI and Windsat

The Global Precipitation Measurement (GPM) mission requires a high degree of consistency among the microwave radiometers in the constellation which, in turn, demands a standard against which all the sensors can be compared. Ultimately this standard will be the GPM Microwave Imager, but for the present the TRMM Microwave Imager (TMI) fills this need. Since its calibration leaves much to be desired, a refinement using Windsat has been developed. This article defines the Consensus Calibration 1.1 which is applied to the TMI. In turn the TMI serves as a transfer standard to other satellite radiometers.