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Christian Kummerow and Louis Giglio

Abstract

This paper describes a multichannel physical approach for retrieving rainfall and vertical structure information from satellite-based passive microwave observations. The algorithm makes use of statistical inversion techniques based upon theoretically calculated relations between rainfall rates and brightness temperatures. Potential errors introduced into the theoretical calculations by the unknown vertical distribution of hydrometeors are overcome by explicitly accounting for diverse hydrometcor profiles. This is accomplished by allowing for a number of different vertical distributions in the theoretical brightness temperature calculations and requiring consistency between the observed and calculated brightness temperatures. This paper will focus primarily on the theoretical aspects of the retrieval algorithm, which includes a procedure used to account for inhomogeneities of the rainfall within the satellite field of view as well as a detailed description of the algorithm as it is applied over both ocean and land surfaces. The residual error between observed and calculated brightness temperatures is found to be an important quantity in assessing the uniqueness of the solution. At is further found that the residual error is a meaningful quantity that can be used to derive expected accuracies from this retrieval technique. Examples comparing the retrieved results as well as the detailed analysis of the algorithm performance under various circumstances are the subject of a companion paper.

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Christian Kummerow and Louis Giglio

Abstract

A multichannel physical approach for retrieving rainfall and its vertical structure from SSM/I observations is examined. While a companion paper was devoted exclusively to the description of the algorithm, its strengths, and its limitations, the main focus of this paper is to report on the results, applicability, and expected accuracies from this algorithm. Some examples are given that compare retrieved results with ground-based radar data from different geographical regions to illustrate the performance and utility of the algorithm under distinct rainfall conditions. Move quantitative validation is accomplished using two months of radar data from Darwin, Australia, and the radar network over Japan. Instantaneous comparisons at Darwin indicate that root-mean-square errors for 1.25° areas over water are 0.09 mm h−1 compared to the mean rainfall value of 0.224 mm h−1 while the correlation exceeds 0.9. Similar results are obtained over the Japanese validation site with rms errors of 0.6 1 5 mm h−1 compared to the mean of 0.880 mm h−1 and a correlation of 0.9. Results are less encouraging over land with root-mean-square errors somewhat larger than the mean rain rates and correlations of only 0.71 and 0.62 for Darwin and Japan, respectively. These validation studies are further used in combination with the theoretical treatment of expected accuracies developed in the companion paper to define error estimates on a broader scale than individual radar sites from which the errors may be analyzed. Comparisons with simpler techniques that are based on either emission or scattering measurements are used to illustrate the fact that the current algorithm, while better correlated with the emission methods over water, cannot be reduced to either of these simpler methods.

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Christian Kummerow and Louis Giglio

Abstract

Passive microwave observations of rainfall offer the ability to obtain very accurate instantaneous estimates of rainfall. Because passive microwave instruments are confined to polar-orbiting satellites, however, such estimates must interpolate across long time periods, during which no measurements are available. In this paper the authors discuss a technique that allows one to partially overcome the sampling limitations by using frequent infrared observations from geosynchronous platforms. To accomplish this, the technique compares all coincident microwave and infrared observations. From each coincident pair, the infrared temperature threshold is selected that corresponds to an area equal to the raining area observed in the microwave image. The mean conditional rainfall rate as determined from the microwave image is then assigned to pixels in the infrared image that are colder than the selected threshold. The calibration is also applied to a fixed threshold of 235 K for comparison with established infrared techniques. Once a calibration is determined, it is applied to all infrared images. Monthly accumulations for both methods are then obtained by summing rainfall from all available infrared images. Two examples are used to evaluate the performance of the technique. The first consists of a one-month period (February 1988) over Darwin, Australia, where good validation data are available from radar and rain gauges. For this case it was found that the technique approximately doubled the rain inferred by the microwave method alone and produced exceptional agreement with the validation data. The second example involved comparisons with atoll rain gauges in the western Pacific for June 1989. Results here are overshadowed by the fact that the hourly infrared estimates from established techniques, by themselves, produced very good correlations with the rain gauges. The calibration technique was not able to improve upon these results.

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Jeffrey T. Morisette, Louis Giglio, Ivan Csiszar, Alberto Setzer, Wilfrid Schroeder, Douglas Morton, and Christopher O. Justice

Abstract

Fire influences global change and tropical ecosystems through its connection to land-cover dynamics, atmospheric composition, and the global carbon cycle. As such, the climate change community, the Brazilian government, and the Large-Scale Biosphere–Atmosphere (LBA) Experiment in Amazonia are interested in the use of satellites to monitor and quantify fire occurrence throughout Brazil. Because multiple satellites and algorithms are being utilized, it is important to quantify the accuracy of the derived products. In this paper the characteristics of two fire detection algorithms are evaluated, both of which are applied to Terra’s Moderate Resolution Imagine Spectroradiometer (MODIS) data and with both operationally producing publicly available fire locations. The two algorithms are NASA’s operational Earth Observing System (EOS) MODIS fire detection product and Brazil’s Instituto Nacional de Pesquisas Espaciais (INPE) algorithm. Both algorithms are compared to fire maps that are derived independently from 30-m spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A quantitative comparison is accomplished through logistic regression and error matrices. Results show that the likelihood of MODIS fire detection, for either algorithm, is a function of both the number of ASTER fire pixels within the MODIS pixel as well as the contiguity of those pixels. Both algorithms have similar omission errors and each has a fairly high likelihood of detecting relatively small fires, as observed in the ASTER data. However, INPE’s commission error is roughly 3 times more than that of the EOS algorithm.

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William S. Olson, Christian D. Kummerow, Gerald M. Heymsfield, and Louis Giglio

Abstract

Three-dimensional tropical squall-line simulations from the Goddard cumulus ensemble (GCE) model are used as input to radiative computations of upwelling microwave brightness temperatures and radar reflectivities at selected microwave sensor frequencies. These cloud/radiative calculations form the basis of a physical cloud/precipitation profile retrieval method that yields estimates of the expected values of the hydrometeor water contents. Application of the retrieval method to simulated nadir-view observations of the aircraft-borne Advanced Microwave Precipitation Radiometer (AMPR) and NASA ER-2 Doppler radar (EDOP) produce random errors of 23%, 19%, and 53% in instantaneous estimates of integrated precipitating liquid, integrated precipitating ice, and surface rain rate, respectively.

On 5 October 1993, during the Convection and Atmospheric Moisture Experiment (CAMEX), the AMPR and EDOP were used to observe convective systems in the vicinity of the Florida peninsula. Although the AMPR data alone could be used to retrieve cloud and precipitation vertical profiles over the ocean, retrievals of high-resolution vertical precipitation structure and profile information over land required the combination of AMPR and EDOP observations.

No validation data are available for this study; however, the retrieved precipitation distributions from the convective systems are compatible with limited radar climatologies of such systems, as well as being radiometrically consistent with both the AMPR and EDOP observations. In the future, the retrieval method will be adapted to the passive and active microwave measurements from the Tropical Rainfall Measuring Mission (TRMM) satellite sensors.

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Wilfrid Schroeder, Jeffrey T. Morisette, Ivan Csiszar, Louis Giglio, Douglas Morton, and Christopher O. Justice

Abstract

Correctly characterizing the frequency and distribution of fire occurrence is essential for understanding the environmental impacts of biomass burning. Satellite fire detection is analyzed from two sensors—the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-12 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua platforms, for 2001–03—to characterize fire activity in Brazil, giving special emphasis to the Amazon region. In evaluating the daily fire counts, their dependence on variations in satellite viewing geometry, overpass time, atmospheric conditions, and fire characteristics were considered. Fire counts were assessed for major biomes of Brazil, the nine states of the Legal Amazon, and two important road corridors in the Amazon region. All three datasets provide consistent information on the timing of peak fire activity for a given state. Also, ranking by relative fire counts per unit area highlights the importance of fire in smaller biomes such as Complexo do Pantanal. The local analysis of road corridors shows trends for fire detections with the increasing intensity of land use. Although absolute fire counts differ by as much as 1200%, when summarized over space and time, trends in fire counts among the three datasets show clear patterns of fire dynamics. The fire dynamics that are evident in these trend analyses are important foundations for assessing environmental impacts of biomass burning and policy measures to manage fire in Brazil.

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Maria Zubkova, Louis Giglio, Michael L. Humber, Joanne V. Hall, and Evan Ellicott

Abstract

It has been 10 years since the start of the Syrian uprisings. While relative stability is improving overall, a new disaster, wildfires, impacted already food-insecure population by burning through key production areas damaging crops, soil, livestock, and deteriorating air quality. Based on remotely sensed data, fire affected 4.8% of Syria in 2019, compared to the average 0.2%, and most fires were observed within agricultural land in the northeast. Abnormal amounts of rainfall during the 2019 growing season and, consequently, high soil moisture explained about 62% of the drastic increase in the burned area extent. In contrast, in 2020, fires continued despite the average amount of rainfall. Extremely high temperature could partially explain a 10-fold increase in the extent of burned area in 2020 but only within forested regions in the northwest. We argue that the abrupt changes in Syria’s fire activity were driven by the complex interaction between conflict, migration, land use, and climate. From one side, the ongoing conflict leads to a drastic increase in the number of accidental and deliberate fires and reduced capacity for fire response. On another side, years of insecurity, widespread displacement, and economic instability left no choice for locals other than exploiting fires to remove natural vegetation for expanding farming, logging, and charcoal trading. The loss of agricultural production and natural vegetation due to fire can have serious implications for food security, soil property, biodiversity, and ecosystem services, which can further exacerbate the already unstable economy and make ongoing violence even more intense.

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