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Hartmut H. Aumann, Alexander Ruzmaikin, and Ali Behrangi

Abstract

The global-mean top-of-atmosphere incident solar radiation (ISR) minus the outgoing longwave radiation (OLR) and the reflected shortwave radiation (RSW) is the net incident radiation (NET). This study analyzes the global-mean NET sensitivity to a change in the global-mean surface temperature by applying the interannual anomaly correlation technique to 9 yr of Atmospheric Infrared Sounder (AIRS) global measurements of RSW and OLR under cloudy and clear conditions. The study finds the observed sensitivity of NET that includes the effects of clouds to be −1.5 ± 0.25 (1σ) W m−2 K−1 and the clear NET sensitivity to be −2.0 ± 0.2 (1σ) W m−2 K−1, consistent with previous work using Earth Radiation Budget Experiment and Clouds and the Earth’s Radiant Energy System data. The cloud effect, +0.5 ± 0.2 (1σ) W m−2 K−1, is a positive component of the NET sensitivity. The similarity of the NET sensitivities derived from forced and unforced models invites a comparison between the observed sensitivities and the effective sensitivities calculated for the Fourth Assessment Report models, although this requires some caution: The effective model sensitivities with clouds range from −0.88 to −1.64 W m−2 K−1, the clear NET sensitivity in the models ranges from −2.32 to −1.73 W m−2 K−1, and the cloud forcing sensitivities range from +0.14 to +1.18 W m−2 K−1. The effective NET and clear NET sensitivities derived from the models are statistically consistent with those derived from the AIRS data, considering the observational and model derivation uncertainties.

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Ali Behrangi, Terry Kubar, and Bjorn Lambrigtsen

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Two years of tropical oceanic cloud observations are analyzed using the operational CloudSat cloud classification product and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Relationships are examined between cloud types, sea surface temperature (SST), and location during the CloudSat early morning and afternoon overpasses. Based on CloudSat and combined lidar–radar products, the maximum and minimum cloud fractions occur at SSTs near 303 and 299 K, respectively, corresponding to deep convective/detrained cloud populations and the transition from shallow to deep convection. For SSTs below approximately 301 K, low clouds (stratiform and stratocumulus) are dominant (cloud fraction between 0.15 and 0.37) whereas high clouds are dominant for SSTs greater than about 301 K (cloud fraction between 0.18 and 0.28). Consistent with previous studies, most tropical low clouds are associated with lower SSTs, with a strong inverse linear relationship between low cloud frequency and SST. For all cloud types except nimbostratus, stratus, and stratocumulus, a sharp increase in frequency of occurrence is observed for SSTs between 299 and 300.5 K, deduced as the onset of deeper convection. Peak fractions of high, deep convective, altostratus, and altocumulus clouds occur at SSTs close to 303 K, while cumulus clouds, which have lower tops, show a smooth cloud fractional peak about 2° cooler. Deep convective and other high cloud types decrease sharply above SSTs of 303 K, in accordance with previous work suggesting a narrow window of tropical deep convection. Finally, significant cloud frequency differences exist between CloudSat early morning and afternoon overpasses, suggesting a diurnal cycle of some cloud types, particularly stratocumulus, high, and deep convective clouds.

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Ali Behrangi, Hai Nguyen, and Stephanie Granger

Abstract

In the present work, a probabilistic ensemble method using the bootstrap is developed to predict the future state of the standard precipitation index (SPI) commonly used for drought monitoring. The methodology is data driven and has the advantage of being easily extended to use more than one variable as predictors. Using 110 years of monthly observations of precipitaton, surface air temperature, and the Niño-3.4 index, the method was employed to assess the impact of the different variables in enhancing the prediction skill. A predictive probability density function (PDF) is produced for future 6-month SPI, and a log-likelihood skill score is used to cross compare various combination scenarios using the entire predictive PDF and with reference to the observed values set aside for validation. The results suggest that the multivariate prediction using complementary information from 3- and 6-month SPI and initial surface air temperature significantly improves seasonal prediction skills for capturing drought severity and delineation of drought areas based on observed 6-month SPI. The improvement is observed across all seasons and regions over the continental United States relative to other prediction scenarios that ignore the surface air temperature information.

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Ali Behrangi, Koulin Hsu, Bisher Imam, and Soroosh Sorooshian

Abstract

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitude–longitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bispectral (visible and infrared) rain estimation scenarios were compared to investigate the technique’s ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude–longitude) scales. Overall, the results using daytime data during June–August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively.

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Guoqiang Tang, Ali Behrangi, Ziqiang Ma, Di Long, and Yang Hong

Abstract

Precipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain–snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature T a downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim T a is downscaled from the original 0.75° to 0.1°. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of T a. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature T w, for rain–snow classification, using T a and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate T w compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain–snow partitioning is conducted using a critical threshold of T w with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based T w using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality T a and T w with high resolution and improving rain–snow partitioning, particularly in mountainous regions.

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Hooman Ayat, Jason P. Evans, Steven Sherwood, and Ali Behrangi

Abstract

High-resolution datasets offer the potential to improve our understanding of spatial and temporal precipitation patterns and storm structures. The goal of this study is to evaluate the similarities and differences of object-based storm characteristics as observed using space- or land-based sensors. The Method of Object-based Diagnostic Evaluation (MODE) Time Domain (MTD) is used to identify and track storm objects in two high-resolution merged datasets: the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) final product V06B and gauge-corrected ground-radar-based Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimations. Characteristics associated with landfalling hurricanes were also examined as a separate category of storm. The results reveal that IMERG and MRMS agree reasonably well across many object-based storm characteristics. However, there are some discrepancies that are statistically significant. MRMS storms are more concentrated, with smaller areas and higher peak intensities, which implies higher flash flood risks associated with the storms. On the other hand, IMERG storms can travel longer distances with a higher volume of precipitation, which implies higher risk of riverine flooding. Agreement between the datasets is higher for faster-moving hurricanes in terms of the averaged intensity. Finally, MRMS indicates a higher average precipitation intensity during the hurricane’s lifetime. However, in non-hurricanes, the opposite result was observed. This is likely related to MRMS having higher resolution; monitoring the hurricanes from many viewing angles, leading to different signal saturation properties compared to IMERG; and/or the dominance of droplet aggregation effects over evaporation effects at lower altitudes.

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Ali Behrangi, Kuo-lin Hsu, Bisher Imam, Soroosh Sorooshian, and Robert J. Kuligowski

Abstract

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network–based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June–August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels—particularly channels 3 (6.5 μm) and 4 (10.7 μm)—resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms.

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Ali Behrangi, Bin Guan, Paul J. Neiman, Mathias Schreier, and Bjorn Lambrigtsen

Abstract

Atmospheric rivers (ARs) are often associated with extreme precipitation, which can lead to flooding or alleviate droughts. A decade (2003–12) of landfalling ARs impacting the North American west coast (between 32.5° and 52.5°N) is collected to assess the skill of five commonly used satellite-based precipitation products [T3B42, T3B42 real-time (T3B42RT), CPC morphing technique (CMORPH), PERSIANN, and PERSIANN–Cloud Classification System (CCS)] in capturing ARs’ precipitation rate and pattern. AR detection was carried out using a database containing twice-daily satellite-based integrated water vapor composite observations. It was found that satellite products are more consistent over ocean than land and often significantly underestimate precipitation rate over land compared to ground observations. Incorrect detection of precipitation from IR-based methods is prevalent over snow and ice surfaces where microwave estimates often show underestimation or missing data. Bias adjustment using ground observation is found very effective to improve satellite products, but it also raises concern regarding near-real-time applicability of satellite products for ARs. The analysis using individual case studies (6–8 January and 13–14 October 2009) and an ensemble of AR events suggests that further advancement in capturing orographic precipitation and precipitation over cold and frozen surfaces is needed to more reliably quantify AR precipitation from space.

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Ali Behrangi, Graeme Stephens, Robert F. Adler, George J. Huffman, Bjorn Lambrigtsen, and Matthew Lebsock

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This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua’s Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors’ estimate for 3-yr (2007–09) near-global (80°S–80°N) oceanic mean precipitation rate is ~2.94 mm day−1. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68 mm day−1) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82 mm day−1). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCP and CMAP, especially in the Southern Hemisphere.

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Ali Behrangi, Bisher Imam, Kuolin Hsu, Soroosh Sorooshian, Timothy J. Bellerby, and George J. Huffman

Abstract

A new multiplatform multisensor satellite rainfall estimation technique is proposed in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to advect microwave (MW)-derived precipitation estimates along cloud motion streamlines and to further adjust the rainfall rates using local cloud classification. The main objective of the Rain Estimation using Forward-Adjusted advection of Microwave Estimates (REFAME) is to investigate whether inclusion of GEO-IR information can help to improve the advected MW precipitation rate as it gets farther in time from the previous MW overpass. The technique comprises three steps. The first step incorporates a 2D cloud tracking algorithm to capture cloud motion streamlines through successive IR images. The second step classifies cloudy pixels to a number of predefined clusters using brightness temperature (Tb) gradients between successive IR images along the cloud motion streamlines in combination with IR cloud-top brightness temperatures and textural features. A mean precipitation rate for each cluster is calculated using available MW-derived precipitation estimates. In the third step, the mean cluster precipitation rates are used to adjust MW precipitation intensities advected between available MW overpasses along cloud motion streamlines. REFAME is a flexible technique, potentially capable of incorporating diverse precipitation-relevant information, such as multispectral data. Evaluated over a range of spatial and temporal scales over the conterminous United States, the performance of the full REFAME algorithm compared favorably with products incorporating either no cloud tracking or no intensity adjustment. The observed improvements in root-mean-square error and especially in correlation coefficient between REFAME outputs and ground radar observations demonstrate that the new approach is effective in reducing the uncertainties and capturing the variation of precipitation intensity along cloud advection streamlines between MW sensor overpasses. An extended REFAME algorithm combines the adjusted advected MW rainfall rates with infrared-derived precipitation rates in an attempt to capture precipitation events initiating and decaying during the interval between two consecutive MW overpasses. Evaluation statistics indicate that the extended algorithm is effective to capture the life cycle of the convective precipitation, particularly for the interval between microwave overpasses in which precipitation starts or ends.

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