On requirements for a satellite mission to measure tropical rainfall

Tropical rainfall data are crucial in determining the role of tropical latent heating in driving the circulation of the global atmosphere. Also, the data are particularly important for testing the realism of climate models, and their ability to simulate and predict climate accurately on the seasonal time scale. Other scientific issues such as the effects of El Nino on climate could be addressed with a reliable, extended time series of tropical rainfall observations. A passive microwave sensor is planned to provide information on the integrated column precipitation content, its areal distribution, and its intensity. An active microwave sensor (radar) will define the layer depth of the precipitation and provide information about the intensity of rain reaching the surface, the key to determining the latent heat input to the atmosphere. A visible/infrared sensor will provide very high resolution information on cloud coverage, type, and top temperatures and also serve as the link between these data and the long and virtually continuous coverage by the geosynchronous meteorological satellites. The unique combination of sensor wavelengths, coverages, and resolving capabilities together with the low-altitude, non-Sun synchronous orbit provide a sampling capability that should yield monthly precipitation amounts to a reasonable accuracy over a 500- by 500-km grid

Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain Rate Estimates at Different Spatial Scales

This study provides a comprehensive inter-comparison of instantaneous rain estimates from the two rain sensors aboard the TRMM satellite with ground data from thee designated Ground Validation Sites: Kwajalein Atoll, Melbourne, Florida and Houston, Texas. The satellite rain retrievals utilize rain observations collected by the TRMM microwave imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard instantaneous rain products are the generated from the rain information retrieved from the satellite using the TMI, PR and Combined (COM) rain algorithms. The validation data set used in this study was obtained from instantaneous rain rates inferred from ground radars at each GV site. The first comparison used 0.5(sup 0) x 0.5(sup 0) gridded data obtained from the TRMM 3668 product, and similarly gridded GV data obtained from ground-based radars. The comparisons were made at the same spatial and temporal scales in order to eliminate sampling biases in our comparisons. An additional comparison was made by averaging rain rates for the PR, COM and GV estimates within each TMI footprint (approx. 150 square kilometers). For this analysis, unconditional mean rain rates from PR, COM and GV estimates were calculated within each TMI footprint that was observed within 100 km from the respective GV site (and also observed by the PR). This analysis used all the available matching data from the period 1999-2004, representing a sample size of over 50,000 footprints for each site. In the first analysis our results showed that all of the respective rain rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm per hour over the ocean. This mode was noted over ocean areas of Melbourne, Florida and Kwajalein, Republic of the Marshall Islands, and is shown to exist in TRMM tropical-global ocean areas as well. Further research by algorithm developers is needed to explain or justify the seemingly errant observed probability distributions

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate

Method to combine spaceborne radar and radiometric observations of precipitation, A

2010 Fall.Includes bibliographical references.This dissertation describes the development and application of a combined radar-radiometer rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) satellite. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution (PSD), and cloud water path (cLWP) are retrieved for each radar profile. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, FL shows agreement within 2% which exceeds previous algorithms' ability to match rainfall at these two sites. The algorithm is then applied to two years of TRMM data over oceans to determine the sources of DSD variability. Three correlated sets of variables representing storm dynamics, background environment, and cloud microphysics are found to account for approximately 50% of the variability in the absolute and reflectivity-normalized median drop size. Structures of radar reflectivity are also identified and related to drop size, with these relationships being confirmed by ground-based polarimetric radar data from the North American Monsoon Experiment (NAME). Regional patterns of DSD and the sources of variability identified herein are also shown to be consistent with previous work documenting regional DSD properties. In particular, mid-latitude regions and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Intertropical Convergence Zone. Due to properties of the DSD and rain water/cloud water partitioning that change with column water vapor, it is shown that increases in water vapor in a global warming scenario could lead to slight (1%) underestimates of a rainfall trends by radar but larger overestimates (5%) by radiometer algorithms. Further analyses are performed to compare tropical oceanic mean rainfall rates between the combined algorithm and other sources. The combined algorithm is 15% higher than the version 6 of the 2A25 radar-only algorithm and 6.6% higher than the Global Precipitation Climatology Project (GPCP) estimate for the same time-space domain. Despite being higher than these two sources, the combined total is not inconsistent with estimates of the other components of the energy budget given their uncertainties

TRMM (Tropical Rainfall Measuring Mission): A satellite mission to measure tropical rainfall

The Tropical Rainfall Measuring Mission (TRMM) is presented. TRMM is a satellite program being studied jointly by the United States and Japan which would carry out the systematic study of tropical rainfall required for major strides in weather and climate research. The scientific justification for TRMM is discussed. The implementation process for the scientific community, NASA management, and the other decision-makers and advisory personnel who are expected to evaluate the priority of the project is outlined

Evaluation of Long-Term SSM/I-Based Precipitation Records over Land

The record of global precipitation mapping using Special Sensor Microwave Imager (SSM/I) measurements now extends over two decades. Similar measurements, albeit with different retrieval algorithms, are to be used in the Global Precipitation Measurement (GPM) mission as part of a constellation to map global precipitation with a more frequent data refresh rate. Remotely sensed precipitation retrievals are prone to both magnitude (precipitation intensity) and phase (position) errors. In this study, the ground-based radar precipitation product from the Next Generation Weather Radar stage-IV (NEXRAD-IV) product is used to evaluate a new metric of error in the long-term SSM/I-based precipitation records. The new metric quantifies the proximity of two multidimensional datasets. Evaluation of the metric across the years shows marked seasonality and precipitation intensity dependence. Drifts and changes in the instrument suite are also evident. Additionally, the precipitation retrieval errors conditional on an estimate of background surface soil moisture are estimated. The dynamic soil moisture can produce temporal variability in surface emissivity, which is a source of error in retrievals. Proper filtering has been applied in the analysis to differentiate between the detection error and the retrieval error. The identification of the different types of errors and their dependence on season, intensity, instrument, and surface conditions provide guidance to the development of improved retrieval algorithms for use in GPM constellation-based precipitation data products

Assessment of High-Resolution Satellite-Based Rainfall Estimates over the Mediterranean during Heavy Precipitation Events

Abstract Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area

Global Precipitation Measurement

This chapter begins with a brief history and background of microwave precipitation sensors, with a discussion of the sensitivity of both passive and active instruments, to trace the evolution of satellite-based rainfall techniques from an era of inference to an era of physical measurement. Next, the highly successful Tropical Rainfall Measuring Mission will be described, followed by the goals and plans for the Global Precipitation Measurement (GPM) Mission and the status of precipitation retrieval algorithm development. The chapter concludes with a summary of the need for space-based precipitation measurement, current technological capabilities, near-term algorithm advancements and anticipated new sciences and societal benefits in the GPM era

Bayesian Retrieval of Complete Posterior PDFs of Oceanic Rain Rate From Microwave Observations

This paper presents a new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measurements Mission (TRMM) Microwave Imager (TMI) over the ocean, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes Theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance our understanding of theoretical benefits of the Bayesian approach, we have conducted sensitivity analyses based on two synthetic datasets for which the true conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak, due to saturation effects. It is also suggested that the choice of the estimators and the prior information are both crucial to the retrieval. In addition, the performance of our Bayesian algorithm is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate