Search

The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and reanalysis data show that assimilating GMI clear-sky radiance, when it does not overlap with overpasses of other microwave sounders, can improve forecasts of both thermodynamic (e.g., temperature and specific humidity) and dynamic variables (geopotential height and wind field), which in turn lead to better track forecasts and a more realistic hurricane inner-core structure. Even when other microwave sounders are present (e.g., AMSU-A, ATMS, MHS, etc.), the assimilation of GMI still reduces temperature forecast errors in the near-hurricane environment, which has a significant impact on the intensity forecast. [ABSTRACT FROM AUTHOR]

This study cross validates the radar reflectivity Z; the rainfall drop size distribution parameter (median volume diameter Do); and the rainfall rate R estimated from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR), a combined PR and TRMM Microwave Imager (TMI) algorithm (COM), and a C-band dual-polarized ground radar (GR) for TRMM overpasses during the passage of tropical cyclone (TC) and non-TC events over Darwin, Australia. Two overpass events during the passage of TC Carlos and 11 non-TC overpass events are used in this study, and the GR is taken as the reference. It is shown that the correspondence is dependent on the precipitation type whereby events with more (less) stratiform rainfall usually have a positive (negative) bias in the reflectivity and the rainfall rate, whereas in the Do the bias is generally positive but small (large). The COM reflectivity estimates are similar to the PR, but it has a smaller bias in the Do for most of the greater stratiform events. This suggests that combining the TMI with the PR adjusts the Do toward the "correct" direction if the GR is taken as the reference. Moreover, the association between the TRMM estimates and the GR for the two TC events, which are highly stratiform in nature, is similar to that observed for the highly stratiform non-TC events (there is no significant difference), but it differs considerably from that observed for the majority of the highly convective non-TC events. [ABSTRACT FROM AUTHOR]

Instantaneous surface rain rate estimates from the Global Precipitation Measurement (GPM) mission's Dual-Frequency Precipitation Radar (DPR) and combined DPR and multifrequency microwave imager (CMB) version-5 products are compared to those from the Met Office Radarnet 4 system's Great Britain and Ireland (GBI) radar composite product. The spaceborne and ground-based rainfall products are collocated spatially and temporally and compared at 5- and 25-km resolutions over GBI during a 3-yr period (from May 2014 to April 2017). The comparison results are evaluated as a function of both the intensity and variability of precipitation within the DPR field of view and are stratified spatially and seasonally. CMB and DPR products underestimate rain rates with respect to the Radarnet product by 21% and 31%, respectively, when considering 25-km resolution data taken within 75 km of a ground-based radar. Large variability in the discrepancies between spaceborne and ground-based rain rate estimates is the result of limitations of both systems and random errors in the collocation of their measurements. The Radarnet retrieval is affected by issues with measuring the vertical extent of precipitation at far ranges, while the GPM system struggles in properly quantifying orographic precipitation. Part of the underestimation by the GPM products appears to be a consequence of an erroneous DPR clutter identification in the presence of low freezing levels. Both products are susceptible to seasonal variations in performance and decreases in precision with increased levels of heterogeneity within the instruments' field of view. [ABSTRACT FROM AUTHOR]

Enhancements of multivariate postprocessing approaches are presented that generate statistically calibrated ensembles of high-resolution precipitation forecast fields with physically realistic spatial and temporal structures based on precipitation forecasts from the Global Ensemble Forecast System (GEFS). Calibrated marginal distributions are obtained with a heteroscedastic regression approach using censored, shifted gamma distributions. To generate spatiotemporal forecast fields, a new variant of the recently proposed minimum divergence Schaake shuffle technique, which selects a set of historic dates in such a way that the associated analysis fields have marginal distributions that resemble the calibrated forecast distributions, is proposed. This variant performs univariate postprocessing at the forecast grid scale and disaggregates these coarse-scale precipitation amounts to the analysis grid by deriving a multiplicative adjustment function and using it to modify the historic analysis fields such that they match the calibrated coarse-scale precipitation forecasts. In addition, an extension of the ensemble copula coupling (ECC) technique is proposed. A mapping function is constructed that maps each raw ensemble forecast field to a high-resolution forecast field such that the resulting downscaled ensemble has the prescribed marginal distributions. A case study over an area that covers the Russian River watershed in California is presented, which shows that the forecast fields generated by the two new techniques have a physically realistic spatial structure. Quantitative verification shows that they also represent the distribution of subgrid-scale precipitation amounts better than the forecast fields generated by the standard Schaake shuffle or the ECC-Q reordering approaches. [ABSTRACT FROM AUTHOR]

This study comprises the analysis and the interpretation of the coherent and the noncoherent parts of a coherent-on-receive microwave radar at grazing incidence conditions. The Doppler measurement is an extension of standard civil marine radar technology. While intensity images require interpretation based on understanding the underlying imaging mechanism, the Doppler signal measures the motion of an area of sea surface and is therefore closely related to the wave physics. Both the measured Doppler signal and the backscatter intensity signal are suitable for surface inversion and give almost identical surface elevations. A statistical comparison with a nearby buoy showed good correlation for the significant wave height and the peak period. By comparing the Doppler signal and the amplitude in the backscatter, the study amends the understanding of imaging mechanisms in marine radars at grazing incidence. [ABSTRACT FROM AUTHOR]

Discrepancies between Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) oceanic rainfall retrievals are prevalent between El Niño and La Niña conditions with TMI exhibiting systematic shifts in precipitation. To investigate the causality of this relationship, this paper focuses on the evolution of precipitation organization between El Niño and La Niña and their impacts on TRMMprecipitation. The results indicate that discrepancies are related to shifts from isolated deep convection during La Niña toward organized precipitation duringElNiñowith the largest variability occurring in the Pacific basins. During El Niño, organized systems are more frequent, have increased areal coverage of stratiform rainfall, and penetrate deeper into the troposphere compared to La Niña. The increased stratiform raining fraction leads to larger increases inTMI rain rates than PR rain rate retrievals.Reanalysis and water vapor data from the Atmospheric Infrared Sounder (AIRS) indicate that organized systems are aided by midtropospheric moisture increases accompanied by increased convective frequency. During La Niña, tropical rainfall is dominated by isolated deep convection due to drier midtropospheric conditions and strong mid- and upper-level zonal wind shear. To examine tropical rainfall--sea surface temperature relations, regime-based bias corrections derived using ground validation (GV) measurements are applied to the TRMM rain estimates. The robust connection with GV-derived biases and oceanic precipitation leads to a reduction in TMI-PR regional differences and tropics-wide precipitation anomalies.The improved agreement betweenPRandTMI estimates yields positive responses of precipitation to tropical SSTs of 10% 8C-1 and 17% 8C-1, respectively, consistent with 15% 8C-1 from the Global Precipitation Climatology Project (GPCP). [ABSTRACT FROM AUTHOR]

Currently, there are several spaceborne microwave instruments suitable for the detection and quantitative estimation of snowfall. To test and improve retrieval snowfall algorithms, ground validation datasets that combine detailed characterization of snowfall microphysics and spatial precipitation measurements are required. To this endpoint, measurements of snow microphysics are combined with large-scale weather radar observations to generate such a dataset. The quantitative snowfall estimates are computed by applying eventspecific relations between the equivalent reflectivity factor and snowfall rate to weather radar observations. The relations are derived using retrieved ice particle microphysical properties from observations that were carried out at the University of Helsinki research station in Hyytiälä, Finland, which is about 64 km east of the radar. For each event, the uncertainties of the estimate are also determined. The feasibility of using this type of data to validate spaceborne snowfall measurements and algorithms is demonstrated with the NASA GPM Microwave Imager (GMI) snowfall product. The detection skill and retrieved surface snowfall precipitation of the GPROF detection algorithm, versions V04A and V05A, are assessed over southern Finland. On the basis of the 26 studied overpasses, probability of detection (POD) is 0.90 for version V04A and 0.84 for version V05A, and corresponding false-alarm rates are 0.09 and 0.10, respectively. A clear dependence of detection skill on cloud echo top height is shown: POD increased from 0.8 to 0.99 (V04A) and from0.61 to 0.94 (V05A) as the cloud echo top altitude increased from 2 to 5 km. Both versions underestimate the snowfall rate by factors of 6 (V04A) and 3 (V05A). [ABSTRACT FROM AUTHOR]

The simulation of observations-a critical Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) component-is validated first by a comparison of error-free simulated observations for the first 24 h at the start of the nature run (NR) to the real observations for those sensors that operated during that period. Sample results of this validation are presented here for existing low-Earth-orbiting (LEO) infrared (IR) and microwave (MW) brightness temperature (BT) observations, for radio occultation (RO) bending angle observations, and for various types of conventional observations. For sensors not operating at the start of the NR, a qualitative validation is obtained by comparing geographic and statistical characteristics of observations over the initial day for such a sensor and an existing similar sensor. The comparisons agree, with no significant unexplained bias, and to within the uncertainties caused by real observation errors, time and space collocation differences, radiative transfer uncertainties, and differences between the NR and reality. To validate channels of a proposed future MW sensor with no equivalent existing spaceborne sensor channel, multiple linear regression is used to relate these channels to existing similar channels. The validation then compares observations simulated from the NR to observations predicted by the regression relationship applied to actual real observations of the existing channels. Overall, the CGOP simulations of error-free observations from conventional and satellite platforms that make up the global observing system are found to be reasonably accurate and suitable as a starting point for creating realistic simulated observations for OSSEs. These findings complete a critical step in the CGOP validation, thereby reducing the caveats required when interpreting the OSSE results. [ABSTRACT FROM AUTHOR]

Prominent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high-frequency (e.g., 89 GHz) brightness temperature depression (i.e., the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g.,230% over the United States) observed during extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution, and aerosol concentrations to form a stronger relationship between precipitation and the scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use the large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduces the difference between the assumed and observed variability in the ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the United States, the results demonstrate outstanding potential in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20%-30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) plays an important role in monitoring global precipitation. In this study, an along-track striping noise is found in GMI observations of brightness temperatures for the two highest-frequency channels-12 and 13-with their central frequencies centered at 183.31 GHz. These two channels are designed for sounding the water vapor in the middle and upper troposphere. The pitch maneuver data of deep space confirmed an existence of striping noise in channels 12 and 13. A striping noise mitigation method is used for extracting the striping noise from the earth scene or deep space measurements of brightness temperatures by combining the principle component analysis (PCA) with the ensemble empirical mode decomposition (EEMD) method. A power spectrum density analysis indicated that the frequency of striping noise ranges between 0.06 and 0.533 s−1, where the right bound of 0.533 s−1 of frequency is exactly the inverse of the time (i.e., 1.875 s) it takes for the GMI to complete one conical scan line. The magnitude of striping noise in the brightness temperature observations of GMI channels 12 and 13 is about ±0.3 K. It is shown that after striping noise mitigation, the observation minus model simulation (O − B) distributions of both the earth scene and deep space brightness temperatures show no visible striping features. [ABSTRACT FROM AUTHOR]

This study uses Global Precipitation Measurement (GPM)Microwave Imager (GMI) and Ka-precipitation radar observations to quantify the snowfall detection performance for different channel (frequency) combinations. Results showed that the low-frequency-channel set contains limited snow detection information with a 0.34 probability of detection (POD). Much better performance is evident using the high-frequency channels (i.e., POD=0.74). In addition, if only one high-frequency channel is allowed to be added to the lowfrequency-channel set, adding the 183±3 GHz channel presents the largest POD improvement (from 0.34 to 0.50). However, this does not imply that the water vapor is the key information for snowfall detection. Only using the high-frequency water vapor channels showed poor snowfall detection with POD at 0.13. Further analysis of all 8191 possible GMI channel combinations showed that the 166-GHz channels are indispensable for any channel combination with POD greater than 0.70. This suggests that the scattering signature, not the water vapor effect, is essential for snowfall detection. Data analysis and model simulation support this explanation. Finally, the GPM constellation radiometers are grouped into six categories based on the channel availability and their snowfall detection capability is estimated, using channels available on GMI. It is found that type-4 radiometer (all channels) has the best snowfall detection performance with a POD of 0.77. The POD values are only slightly smaller for the type-3 radiometer (high-frequency channels) and type-5 radiometer (all channels except 183 channels). [ABSTRACT FROM AUTHOR]

The Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1. [ABSTRACT FROM AUTHOR]

Rainfall is a fundamental process in the hydrologic cycle. This study investigated the cause-effect relationship in which precipitation at lower frequencies affects the amount of emitted radiation and at higher frequencies affects the amount of backscattered terrestrial radiation. Because the advantage of a probabilistic graphical model is its graphical representation, which allows easy causality interpretation using the arc directions, two Bayesian networks (BNs) were used, namely, a naïve Bayes classifier and a tree-augmented naïve Bayes model. To empirically evaluate and compare BN-based models, 'black box'-based models, including nearest-neighbor searches and artificial neural network (ANN)-based multilayer perceptron and logistic regression, were used as benchmarks. For the two study regions-namely, the Tanshui River basin in northern Taiwan and Chianan Plain in southern Taiwan-rain occurrences during typhoon seasons were examined using passive microwave imagery recorded using the Special Sensor Microwave Imager/Sounder. The results show that although black box models exhibit excellent prediction ability, interpretation of their behavior is unsatisfactory. By contrast, probabilistic graphical models can explicitly reveal the causal relationship between brightness temperatures and nonrain/rain discrimination. For the Tanshui River basin, 19.35-, 22.23-, 37.0-, and 85.5-GHz vertically polarized brightness temperatures were found to diagnose rain occurrences. For the Chianan Plain, a more sensitive indicator of rain-scattering signals was obtained using 85-GHz measurements. The results demonstrate the potential use of BNs in identifying rain occurrences in regions with land features comprising various absorbing and scattering materials. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) satellite began operating in December 1997 and was shut down on 8 April 2015. Over the oceans, the microwave (MW) sensor aboard TRMM measures sea surface temperature, wind speed, and rain rate as well as atmospheric columnar water vapor and cloud liquid water. Improved calibration methods are applied to the TRMM Microwave Imager (TMI), and a 17-yr climate record of these environmental parameters is produced so as to be consistent with the climate records from 13 other MW sensors. These TMI retrievals are validated relative to in situ observations over its 17-yr mission life. All indications point to TMI being an extremely stable sensor capable of providing satellite climate records of unprecedented length and accuracy. [ABSTRACT FROM AUTHOR]

This study analyzes 25 years of Special Sensor Microwave Imager (SSM/I) retrievals of rain rate and wind speed to assess changes in storminess over the open water of the Pacific Ocean. Changes in storminess are characterized by combining trends in both the statistically derived 95th percentile exceedance frequencies of rain rate and wind speed (i.e., extremes). Storminess is computed annually and seasonally, with further partitioning done by phase of the El Niño-Southern Oscillation (ENSO) index and the Pacific decadal oscillation (PDO) index. Overall, rain-rate exceedance frequencies of 6-8 mm h−1 cover most of the western and central tropical Pacific, with higher values present around the Philippines, Japan, Mexico, and the northwest coast of Australia. Wind speed exceedance frequencies are a strong function of latitude, with values less (greater) than 12 m s−1 equatorward (poleward) of 30°N/S. Statistically significant increasing trends in rain rate were found in the western tropical Pacific near the Caroline Islands and the Solomon Islands, and in the extratropics from the Aleutian Islands down the coast along British Columbia and Washington State. Statistically significant increasing trends in wind speed are present in the equatorial central Pacific near Kiribati and the Republic of the Marshall Islands (RMI), and in the extratropics along the west coast of the United States and Canada. Thus, while extreme rain and winds are both increasing across large areas of the Pacific, these areas are modulated according to the phase of ENSO and the PDO, and their intersection takes aim at specific locations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rainfall estimates from versions 6 (V6) and 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 algorithms are compared relative to the Next Generation Weather Radar (NEXRAD) Multisensor Precipitation Estimate stage-IV hourly rainfall product. The dataset consists of 252 TRMM overpasses of tropical cyclones from 2002 to 2010 within a 230-km range of southeastern U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) sites. All rainfall estimates are averaged to a uniform 1/7° square grid. The grid boxes are also divided by their TMI surface designation (land, ocean, or coast). A detailed statistical analysis is undertaken to determine how changes to the TRMM rainfall algorithms in the latest version (V7) are influencing the rainfall retrievals relative to ground reference data. Version 7 of the PR 2A25 is the best-performing algorithm over all three surface types. Over ocean, TMI 2A12 V7 is improved relative to V6 at high rain rates. At low rain rates, the new ocean TMI V7 probability-of-rain parameter creates ambiguity in differentiating light rain (≤0.5 mm h−1) and nonraining areas. Over land, TMI V7 underestimates stage IV more than V6 does at a wide range of rain rates, resulting in an increased negative bias. Both versions of the TMI coastal algorithm are also negatively biased at both moderate and heavy rain rates. Some of the TMI biases can be explained by uncertain relationships between rain rate and 85-GHz ice scattering. [ABSTRACT FROM AUTHOR]

The Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program F-16 spacecraft measures the Earth-emitted radiation at frequencies from 19 to 183 GHz. From its high-frequency channels at 91 and 150 GHz, cloud microphysical parameters can be observed at a spatial resolution of 15 km. In this study, a simplified two-stream radiative transfer model is applied for microwave applications as a three-parameter equation and then used to retrieve the ice cloud water path (IWP) and ice particle effective diameter De. Since SSMIS is a conically scanning instrument, the retrieved IWP is less dependent on scan position and is a useful product for imaging atmospheric ice-phase clouds related to precipitation. Thus, IWP is also used to estimate surface rainfall rate through the same relationship derived previously and used in Advanced Microwave Sounding Unit (AMSU-B) and Microwave Humidity Sounder applications. The SSMIS-derived ice cloud products are compared with those from other microwave instruments on the MetOp-A satellite, and both agree well in their spatial distributions. [ABSTRACT FROM AUTHOR]

Like all reanalysis efforts, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) must contend with an inhomogeneous observing network. Here the effects of the two most obvious observing system epoch changes, the Advanced Microwave Sounding Unit-A (AMSU-A) series in late 1998 and, to a lesser extent, the earlier advent of the Special Sensor Microwave Imager (SSM/I) in late 1987 are examined. These sensor changes affect model moisture and enthalpy increments and thus water and energy fluxes, since the latter result from model physics processes that respond sensitively to state variable forcing. Inclusion of the analysis increments in the MERRA dataset is a unique feature among reanalyses that facilitates understanding the relationships between analysis forcing and flux response. In stepwise fashion in time, the vertically integrated global-mean moisture increments change sign from drying to moistening and heating increments drop nearly 15 W m−2 over the 30 plus years of the assimilated products. Regression of flux quantities on an El Niño-Southern Oscillation sea surface temperature (SST) index analysis reveals that this mode of climate variability dominates interannual signals and its leading expression is minimally affected by satellite observing system changes. Conversely, precipitation patterns and other fluxes influenced by SST changes associated with Pacific decadal variability (PDV) are significantly distorted. Observing system changes also induce a nonstationary component to the annual cycle signals. Principal component regression is found useful for identifying artifacts produced by changes of satellite sensors and defining appropriate adjustments. After the adjustments are applied, the spurious flux trend components are greatly diminished. Time series of the adjusted precipitation and the Global Precipitation Climatology Project (GPCP) data compare favorably on a global basis. The adjustments also provide a much better depiction of precipitation spatial trends associated with PDV-like forcing. The utility as well as associated drawbacks of this statistical adjustment and the prospects for future improvements of the methodology are discussed. [ABSTRACT FROM AUTHOR]

Representative patterns from multichannel microwave brightness temperature Tb in the midlatitude oceanic region, observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), are studied during precipitation events detected by the TRMM precipitation radar (PR) for three summer and winter seasons using empirical orthogonal function (EOF) analysis. The first three patterns are interpreted as rain liquid water, solid particles, and rain type based on the frequency distributions of vertical profiles of the radar reflectivity factor and the heights of the storm top, cloud top, and freezing level. The first EOF (EOF1) correlates with the near-surface rain rate. While the eigenvector for the 85.5-GHz channel is less significant for EOF1 variability in summer, those in all channels contribute equally to the variability in winter. This difference suggests that summer precipitation is caused by additional solid particles formed in developing precipitation systems. The second EOF (EOF2) represents the number of solid particles and also corresponds to the near-surface rain rate. This result suggests an increase of solid particles with the development of precipitation systems. EOF2 varies largely by echo-top height in summer and by echo-top height and freezing height in winter. The positive component score has double Tb peaks. Dividing the score into two patterns according to these peaks reveals highly developed precipitation systems, such as convective rainbands and frontal systems, and weak precipitation with shallow systems caused by cold outbreaks in the winter case. The negative component score also shows shallow and weak precipitation systems with warm rain. The third EOF (EOF3) is related to rain type. Vertical profiles show a significant bright band with a small height difference between the echo top and freezing level for negative EOF3, while positive EOF3 has no bright band with a high echo top relative to the freezing height. The results indicate that stratiform and convective precipitation systems can be characterized by EOF3. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) satellite has provided invaluable data for tropical cyclone (TC) research since December 1997. The challenge, however, is how to analyze and efficiently utilize all of the information from several instruments on TRMM that observe the same target. In this study, a tropical cyclone precipitation, cloud, and convective cell feature (TCPF) database has been developed by using observations of the TRMM precipitation radar (PR), Microwave Imager (TMI), Visible and Infrared Scanner (VIRS), Lightning Imaging System (LIS), and the TRMM 3B42 rainfall product. The database is based on an event-based method that analyzes the measurements from multiple sensors. This method condenses the original information of pixel-level measurements into the properties of events, which can significantly increase the efficiency of searching and sorting the observed historical TCs. With both convective and rainfall properties included, the database offers the potential to aid the research aiming to improve both TC intensification and rainfall forecasts. Using the TRMM TCPF database, regional variations of TC convection and diurnal variations of TC rainfall are examined. In terms of absolute number, the northwest Pacific Ocean basin has the deepest and most intense TCPFs according to IR, radar, and 85-GHz microwave measurements. However, the North Atlantic TCPFs appear to have the highest lightning production. Globally, TC rainfall has a maximum at 0430--0730 local solar time (LST) and a minimum around 1930--2230 LST. However, after separating ocean from land, a distinct difference is seen. Over land, the diurnal variation of TC rainfall shows double peaks: one around 0130--0730 LST and the other at 1630--1930 LST. The minimum is at 1030--1330 LST. [ABSTRACT FROM AUTHOR]

A new intersensor calibration scheme is developed for the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) to correct its scan-angle-dependent bias, the radar calibration beacon interference on the F-15 satellite, and other intersensor biases. The intersensor bias is characterized by the simultaneous overpass measurements with the F-13 SSM/I as a reference. This sensor data record (SDR) intersensor calibration procedure is routinely running at the National Oceanic and Atmospheric Administration and is now used for reprocessing all SSM/I environmental data records (EDR), including total precipitable water (TPW) and surface precipitation. Results show that this scheme improves the consistency of the monthly SDR''s time series from different SSM/I sensors. Relative to the matched rain products from the Tropical Rainfall Measuring Mission, the bias of SSM/I monthly precipitation is reduced by 12%% after intersensor calibration. TPW biases between sensors are reduced by 75%% over the global ocean and 20%% over the tropical ocean, respectively. The intersensor calibration reduces biases by 20.6%%, 15.7%%, and 6.5%% for oceanic, land, and global precipitation, respectively. The TPW climate trend is 1.59%% decade−−1 (or 0.34 mm decade−−1) for the global ocean and 1.39%% decade−−1 (or 0.63 mm decade−−1) for the tropical ocean, indicating related trends decrease of 38%% and 54%%, respectively, from the uncalibrated SDRs. Results demonstrate the large impacts of this calibration on the TPW climate trend. [ABSTRACT FROM AUTHOR]

Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and precipitation radar measurements are examined for strong convective systems. Storms having similar values of minimum 37-GHz polarization-corrected temperature (PCT) are grouped together, and their vertical profiles of maximum radar reflectivity are composited. Lower 37-GHz PCT corresponds to stronger radar profiles (high reflectivity through a deep layer), but characteristic profiles for a given 37-GHz PCT are different for deep tropical ocean, deep tropical land, subtropical ocean, and subtropical land regions. Tropical oceanic storms have a sharper decrease of reflectivity just above the freezing level than storms from other regions with the same brightness temperature. Storms from subtropical land regions have the slowest decrease of reflectivity with height and the greatest mixed-phase-layer ice water content (IWC). Linear fits of 37-GHz PCT versus IWC for each region are used to scale the brightness temperatures. Counts of storms with these scaled brightness temperatures below certain thresholds suggest that not as many of the strongest storms occur in central Africa as in subtropical parts of South America, the United States, and central Asia. [ABSTRACT FROM AUTHOR]

Coupled-dipole approximation (CDA) calculations of microwave extinction and radar backscatter are presented for nonhomogeneous (soft) ice spheres and for quasi-realistic aggregates of elementary ice crystal forms, including both simple needles and real dendrites. Frequencies considered include selections from the Dual-Frequency Precipitation Radar (DPR; 13.4 and 35.6 GHz) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI; 18.7, 36.5, and 89.0 GHz), both slated for orbit on the GPM mission. The computational method is first validated against Mie theory using dipole structures representing solid ice spheres as well as stochastically generated “soft” ice spheres of variable ice–air ratio. Neither the traditional Bruggeman nor Maxwell Garnett dielectric mixing formula is found to correctly predict the full range of CDA results for soft spheres. However, an excellent fit is found using the generalized mixing rule of Sihvola with ν = 0.85. The suitability of the soft-sphere approximation for realistic aggregates is investigated, taking into account the spectral dependence of backscatter and/or extinction per unit mass at key DPR and GMI frequencies. Even when spheres of nonequal mass are considered, there is no single combination of fraction and mass that simultaneously captures all the relevant radiative properties. All four aggregate models do, however, exhibit a predictable power-law dependence of the mass extinction coefficient on the total particle mass. Dual-frequency mass extinction ratios are only very weakly dependent on particle masses; moreover, the ratio is found to be approximately proportional to frequency raised to the power 2.5. The dual-frequency backscatter ratio is found to be a predictable function of the aggregate mass for particles smaller than 3 mg. Above this size, the ratio is strongly sensitive to aggregate shape, a finding that raises concerns about the utility of dual-frequency backscatter ratio measurements whenever larger particles might be present in a volume of air. The validity of the Rayleigh–Gans approximation applied to radar backscatter from snow aggregates was also examined. Although the dual-frequency backscatter ratio was reasonably well reproduced, the absolute magnitude was not. [ABSTRACT FROM AUTHOR]

The Indian Ocean exhibits strong variability on a number of time scales, including prominent intraseasonal variations in both the atmosphere and ocean. Of particular interest is the south tropical Indian Ocean thermocline ridge, a region located between 12° and 5°S, which exhibits prominent variability in sea surface temperature (SST) due to dominant winds that raise the thermocline and shoal the mixed layer. In this paper, submonthly (less than 30 day) cooling events in the thermocline ridge region are diagnosed with observations and models, and are related to large-scale conditions in the Indo-Pacific region. Observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite were used to identify 16 cooling events in the period 1998–2007, which on average cannot be fully accounted for by air–sea enthalpy fluxes. Analysis of observations and a hierarchy of models, including two coupled global climate models (GFDL CM2.1 and GFDL CM2.4), indicates that ocean dynamical changes are important to the cooling events. For extreme cooling events (above 2.5 standard deviations), air–sea enthalpy fluxes account for approximately 50% of the SST signature, and oceanic processes cannot in general be neglected. For weaker cooling events (1.5–2.5 standard deviations), air–sea enthalpy fluxes account for a larger fraction of the SST signature. Furthermore, it is found that cooling events are preconditioned by large-scale, low-frequency changes in the coupled ocean–atmosphere system. When the thermocline is unusually shallow in the thermocline ridge region, cooling events are more likely to occur and are stronger; these large-scale conditions are more (less) likely during La Niña (El Niño/Indian Ocean dipole) events. Strong cooling events are associated with changes in atmospheric convection, which resemble the Madden–Julian oscillation, in both observations and the models. [ABSTRACT FROM AUTHOR]

Spaceborne microwave sensors provide critical rain information used in several global multisatellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs 4 yr (2003–06) of satellite data to assess the relative performance and skill of the Special Sensor Microwave Imager [SSM/I (F13, F14, and F15], Advanced Microwave Sounding Unit [AMSU-B (N15, N16, and N17)], Advanced Microwave Scanning Radiometer for Earth Observing System [AMSR-E (Aqua)], and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from the TRMM Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ), and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain-rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB were further stratified into ocean, land, and coast categories using a 0.25° terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating–observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm h-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data were analyzed on monthly scales. These results signal to developers of global rainfall products, such as the TRMM Multisatellite Precipitation Analysis (TMPA) and the Climate Data Center’s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates to provide the highest-quality estimates in their products. [ABSTRACT FROM AUTHOR]

This study compares instantaneous rainfall estimates provided by the current generation of retrieval algorithms for passive microwave sensors using retrievals from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and merged surface radar and gauge measurements over the continental United States as references. The goal is to quantitatively assess surface rain retrievals from cross-track scanning microwave humidity sounders relative to those from conically scanning microwave imagers. The passive microwave sensors included in the study are three operational sounders—the Advanced Microwave Sounding Unit-B (AMSU-B) instruments on the NOAA-15, -16, and -17 satellites—and five imagers: the TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument on the Aqua satellite, and the Special Sensor Microwave Imager (SSM/I) instruments on the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites. The comparisons with PR data are based on “coincident” observations, defined as instantaneous retrievals (spatially averaged to 0.25° latitude and 0.25° longitude) within a 10-min interval collected over a 20-month period from January 2005 to August 2006. Statistics of departures of these coincident retrievals from reference measurements as given by the TRMM PR or ground radar and gauges are computed as a function of rain intensity over land and oceans. Results show that over land AMSU-B sounder rain retrievals are comparable in quality to those from conically scanning radiometers for instantaneous rain rates between 1.0 and 10.0 mm h-1. This result holds true for comparisons using either TRMM PR estimates over tropical land areas or merged ground radar/gauge measurements over the continental United States as the reference. Over tropical oceans, the standard deviation errors are comparable between imager and sounder retrievals for rain intensities above 5 mm h-1, below which the imagers are noticeably better than the sounders; systematic biases are small for both imagers and sounders. The results of this study suggest that in planning future satellite missions for global precipitation measurement, cross-track scanning microwave humidity sounders on operational satellites may be used to augment conically scanning microwave radiometers to provide improved temporal sampling over land without degradation in the quality of precipitation estimates. [ABSTRACT FROM AUTHOR]

The low-frequency variability of gap winds at the Isthmuses of Tehuantepec and Papagayo is investigated using a 17-yr wind stress dataset merging the remotely sensed observations of Special Sensor Microwave Imager (SSM/I) and Quick Scatterometer (QuikSCAT) satellite sensors. A decadal signal is identified in the Tehuantepec gap winds, which is shown to be related to the Atlantic tripole pattern (ATP). Using linear regression and spectral analysis, it is demonstrated that the low-frequency variability of the Tehuantepec gap winds is remotely forced by the ATP, and the Papagayo gap winds are primarily governed by El Niño–Southern Oscillation (ENSO) with the ATP being of secondary importance. The Tehuantepec (Papagayo) time series of wind stress anomalies can be better reconstructed when the local cross-isthmus pressure difference and large-scale climate information such as the ATP (ENSO) are included, suggesting that there is important information in the large-scale flow that is not transmitted directly through the background sea level pressure gradient. The geostrophic modulation of the easterly trades in the western Caribbean also serve as a remote driver of the Papagayo gap winds, which is itself not fully independent from ENSO. Finally, it is suggested that precipitation variability in the Inter-Americas region is closely related to the same remote forcing as that of the Tehuantepec gap winds, being the ATP and associated large-scale atmospheric circulation. [ABSTRACT FROM AUTHOR]

An event-based method of analyzing the measurements from multiple satellite sensors is presented by using observations of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), Microwave Imager (TMI), Visible and Infrared Scanner (VIRS), and Lightning Imaging System (LIS). First, the observations from PR, VIRS, TMI, and LIS are temporally and spatially collocated. Then the cloud and precipitation features are defined by grouping contiguous pixels using various criteria, including surface rain, cold infrared, or microwave brightness temperature. The characteristics of measurements from different sensors inside these features are summarized. Then, climatological descriptions of many properties of the identified features are generated. This analysis method condenses the original information of pixel-level measurements into the properties of events, which can greatly increase the efficiency of searching and sorting the observed historical events. Using the TRMM cloud and precipitation feature database, the regional variations of rainfall contribution by features with different size, intensity, and PR reflectivity vertical structure are shown. Above the freezing level, land storms tend to have larger 20-dBZ area and reach higher altitude than is the case for oceanic storms, especially those land storms over central Africa. Horizontal size and the maximum reflectivity of oceanic storms decrease with altitude. For land storms, these intensity measures increase with altitude between 2 km and the freezing level and decrease more slowly with altitude above the freezing level than for ocean storms. [ABSTRACT FROM AUTHOR]

Boreal summer intraseasonal (30–90-day time scale) sea surface temperature (SST) variability in the east Pacific warm pool is examined using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) sea surface temperatures during 1998–2005. Intraseasonal SST variance maximizes at two locations in the warm pool: in the vicinity of 9°N, 92°W near the Costa Rica Dome and near the northern edge of the warm pool in the vicinity of 19°N, 108°W. Both locations exhibit a significant spectral peak at 50–60-day periods, time scales characteristic of the Madden–Julian oscillation (MJO). Complex empirical orthogonal function (CEOF) and spectra coherence analyses are used to show that boreal summer intraseasonal SST anomalies are coherent with precipitation anomalies across the east Pacific warm pool. Spatial variations of phase are modest across the warm pool, although evidence exists for the northward progression of intraseasonal SST and precipitation anomalies. Intraseasonal SSTs at the north edge of the warm pool lag those in the vicinity of the Costa Rica Dome by about 1 week. The MJO explains 30%–40% of the variance of intraseasonal SST anomalies in the east Pacific warm pool during boreal summer. Peak-to-peak SST variations of 0.8°–1.0°C occur during MJO events. SST is approximately in quadrature with MJO precipitation, with suppressed (enhanced) MJO precipitation anomalies leading positive (negative) SST anomalies by 7–10 days. Consistent with the CEOF and coherence analyses, MJO-related SST and precipitation anomalies near the Costa Rica Dome lead those at the northern edge of the warm pool by about 1 week. [ABSTRACT FROM AUTHOR]

Topographical influences on the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain retrievals over the terrain area of the Korean peninsula were examined using a training dataset constructed from numerical mesoscale model simulations in conjunction with radiative transfer calculations. By relating numerical model outputs to rain retrievals from simulated brightness temperatures, a positive relationship between topographically forced vertical motion and rain retrievals in the upstream region over the mountainous area was found. Based on the relationship obtained, three topographical correction methods were developed by incorporating slope-forced vertical motion and its associated upward vapor flux, and vapor flux convergence in the surface boundary layer into a scattering-based TMI rain retrieval algorithm. The developed correction methods were then applied for the rain retrievals from simulated TMI brightness temperatures with model outputs and measured TMI brightness temperatures. Results showed that orographic influences on the rain formation can be included in the TMI rainfall algorithms, which tend to underestimate rainfall over the complex terrain area. It was shown that topographical corrections surely improve the rain retrieval when a strong rain event is present over the upslope region. Among various elements, moisture convergence in the boundary layer appears to be an important factor needed in the topographical correction. Overall topography-corrected estimates of rainfall showed a better agreement with ground measurements than those without correction, suggesting that satellite rain retrieval over the terrain area can be improved when accurate numerical forecast outputs are incorporated into the rain retrieval algorithm. [ABSTRACT FROM AUTHOR]

This work describes a new climatology of cloud liquid water path (LWP), termed the University of Wisconsin (UWisc) climatology, derived from 18 yr of satellite-based passive microwave observations over the global oceans. The climatology is based on a modern retrieval methodology applied consistently to the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS) (AMSR-E) microwave sensors on eight different satellite platforms, beginning in 1988 and continuing through 2005. It goes beyond previously published climatologies by explicitly solving for the diurnal cycle of cloud liquid water by providing statistical error estimates, and includes a detailed discussion of possible systematic errors. A novel methodology for constructing the climatology is used in which a mean monthly diurnal cycle as well as monthly means of the liquid water path are derived simultaneously from the data on a 1° grid; the methodology also produces statistical errors for these quantities, which decrease toward the end of the time record as the number of observations increases. The derived diurnal cycles are consistent with previous findings in the tropics, but are also derived for higher latitudes and contain more information than in previous studies. The new climatology exhibits differences with previous observationally based climatologies and is found to be more consistent with the 40-yr ECMWF Re-Analysis (ERA-40) than are the previous climatologies. Potential systematic errors of the order of 15%–30% or higher exist in the LWP climatology. A previously unexplored source of systematic error is caused by the assumption that all microwave-based retrievals of LWP must make regarding the partitioning of cloud water and rainwater, which cannot be determined using microwave observations alone. The potentially large systematic errors that result may hamper the usefulness of microwave-based climatologies of both cloud liquid water and especially rain rate, particularly in certain regions of the tropics and midlatitudes where the separation of rain from liquid cloud water is most critical. [ABSTRACT FROM AUTHOR]

The adequacy of hourly rainfall sampling was examined in terms of the detection of diurnal variations using 8 yr (1998–2005) of data observed by the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) satellite. It was found that the monthly and hourly rain samples for each 0.2° grid point over the 8-yr period are composed of multiple precipitation systems. In this study, a “3-h-significant diurnal peak” was defined as the time of maximum rainfall with consecutive positive anomalies for more than 3 h. The fraction of the analyzed area with a 3-h-significant diurnal peak increased annually and accounted for 43% of the total global tropics at 0.2° resolution over the 8-yr period. The diurnal signature over Tibet and the Amazon showed a high degree of spatial uniformity (at >10° scale). The degree of similarity and locations of the regional diurnal characteristics are described in terms of seasonal variations and at multiple resolutions based on spatial uniformity. For example, uniform early-afternoon peaks generally appear over the coastal land and areas of high relief, whereas a seasonally invariant early-afternoon peak over the low-lying Amazon basin is recognized as a regional characteristic. In areas of coastal ocean, early-morning peaks appear in certain regions such as the area surrounding the so-called Maritime Continent and the area off the west coast of Mexico. These peaks are distinct from the global characteristics of late-morning rainfall maxima recorded over most coastal oceans and early-morning peaks recorded over open ocean. The results are also compared with those derived from TRMM Microwave Imager (TMI) data. In addition to obtaining a coherent signal, regional differences in the timing of maximum rainfall over the Tibetan Plateau were addressed; this discrepancy is attributed to limitations of the scattering algorithm used for TMI data in terms of detecting shallow convection and screening cold surfaces. [ABSTRACT FROM AUTHOR]

General circulation models are unable to resolve subgrid-scale moisture variability and associated cloudiness and so must parameterize grid-scale cloud properties. This typically involves various empirical assumptions and a failure to capture the full range (synoptic, geographic, diurnal) of the subgrid-scale variability. A variational parameter estimation technique is employed to adjust empirical model cloud parameters in both space and time, in order to better represent assimilated International Satellite Cloud Climatology Project (ISCCP) cloud fraction and optical depth and Special Sensor Microwave Imager (SSM/I) liquid water path. The value of these adjustments is verified by much improved cloud radiative forcing and persistent improvement in cloud fraction forecasts. [ABSTRACT FROM AUTHOR]

Currently, operational weather forecasting systems use observations to optimize the initial state of a forecast without considering possible model deficiencies. For precipitation assimilation, this could be an issue since precipitation observations, unlike conventional data, do not directly provide information on the atmospheric state but are related to the state variables through parameterized moist physics with simplifying assumptions. Precipitation observation operators are comparatively less accurate than those for conventional data or observables in clear-sky regions, which can limit data usage not because of issues with observations, but with the model. The challenge lies in exploring new ways to make effective use of precipitation data in the presence of model errors. This study continues the investigation of variational algorithms for precipitation assimilation using column model physics as a weak constraint. The strategy is to develop techniques to make online estimation and correction of model errors to improve the precipitation observation operator during the assimilation cycle. Earlier studies have shown that variational continuous assimilation (VCA) of tropical rainfall using moisture tendency correction can improve Goddard Earth Observing System 3 (GEOS-3) global analyses and forecasts. Here results are presented from a 4-yr GEOS-3 reanalysis assimilating Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Special Sensor Microwave Imager (SSM/I) tropical rainfall using the VCA scheme. Comparisons with NCEP operational analysis and the 40-yr ECMWF Re-Analysis (ERA-40) show that the GEOS-3 reanalysis is significantly better at replicating the intensity and variability of tropical precipitation systems ranging from a few days to interannual time scales. As a further refinement of rainfall assimilation using the VCA scheme, a variational algorithm for assimilating TMI latent heating retrievals using semiempirical parameters in the model moist physics as control variables is described and initial test results are presented. [ABSTRACT FROM AUTHOR]

Currently, satellite radiances in the Canadian Meteorological Centre operational data assimilation system are only assimilated in clear skies. A two-step method, developed at the European Centre for Medium-Range Weather Forecasts, is considered to assimilate Special Sensor Microwave Imager (SSM/I) observations in rainy atmospheres. The first step consists of a one-dimensional variational data assimilation (1DVAR) method. Model temperature and humidity profiles are adjusted by assimilating either SSM/I brightness temperatures or retrieved surface rain rates (derived from SSM/I brightness temperatures). In the second step, 1DVAR column-integrated water vapor analyses are assimilated in four-dimensional variational data assimilation (4DVAR). At the Meteorological Service of Canada, such a 1DVAR assimilation system has been developed. Model profiles are obtained from a research version of the Global Environmental Multi-Scale model. Several issues raised while developing the 1DVAR system are addressed. The impact of the size of the observation error is studied when brightness temperatures are assimilated. For two case studies, analyses are derived when either surface rain rate or brightness temperatures are assimilated. Differences in the analyzed fields between these configurations are discussed and shortcomings of each approach are identified. Results of sensitivity studies are also provided. First the impact of observation error correlation between channels is investigated. Second, the size of the background temperature error is varied to assess its impact on the analyzed column-integrated water vapor. Third, the importance of each moist physical scheme is investigated. Finally, the portability of moist physical schemes specifically developed for data assimilation is discussed. [ABSTRACT FROM AUTHOR]

The article presents research into land surface microwave emissivities. A decade-long land surface emissivity dataset was calculated from Special Sensor Microwave Imager (SSM/I) satellite observations between 19 GHz and 85 GHz. Applications for land surface microwave emissivities include atmospheric retrieval over land from satellite passive microwave observations and land surface characterization. The SSM/I instruments were on board the Defense Meteorological Satellite Program (DMS) polar satellites, which make twice daily Earth observations.

Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), TRMM Microwave Imager (TMI), and Visible and Infrared Scanner (VIRS) observations within the Precipitation Feature (PF) database have been analyzed to examine regional variability in rain area and maximum horizontal extent of rainfall features, and role of storm morphology on rainfall production (and thus modes where vertically integrated heating occurs). Particular attention is focused on the sampling geometry of the PR and the resulting impact on PF statistics across the global Tropics. It was found that 9% of rain features extend to the edge of the PR swath, with edge features contributing 42% of total rainfall. However, the area (maximum dimension) distribution of PR features is similar to the wider-swath TMI up until a truncation point of nearly 30 000 km2 (250 km), so a large portion of the feature size spectrum may be examined using the PR as with past ground-based studies. This study finds distinct differences in land and ocean storm morphology characteristics, which lead to important differences in rainfall modes regionally. A larger fraction of rainfall comes from more horizontally and vertically developed PFs over land than ocean due to the lack of shallow precipitation in both relative and absolute frequency of occurrence, with a trimodal distribution of rainfall contribution versus feature height observed over the ocean. Mesoscale convective systems (MCSs) are found to be responsible for up to 90% of rainfall in selected land regions. Tropicswide, MCSs are responsible for more than 50% of rainfall in almost all regions with average annual rainfall exceeding 3 mm day-1. Characteristic variability in the contribution of rainfall by feature type is shown over land and ocean, which suggests new approaches for improved convective parameterizations. [ABSTRACT FROM AUTHOR]

Radiative transfer models for scattering atmospheres that are accurate yet computationally efficient are required for many applications, such as data assimilation in numerical weather prediction. The successive-order-of-interaction (SOI) model is shown to satisfy these demands under a wide range of conditions. In particular, the model has an accuracy typically much better than 1 K for most microwave and submillimeter cases in precipitating atmospheres. Its speed is found to be comparable to or faster than the commonly used though less accurate Eddington model. An adjoint has been written for the model, and so Jacobian sensitivities can be quickly calculated. In addition to a conventional error assessment, the correlation between errors in different microwave channels is also characterized. These factors combine to make the SOI model an appealing candidate for many demanding applications, including data assimilation and optimal estimation, from microwave to thermal infrared wavelengths. [ABSTRACT FROM AUTHOR]

A technique for the assimilation of Special Sensor Microwave Imager (SSM/I) data in the National Centers for Environmental Prediction (NCEP) global data assimilation and forecast system is described. Because the radiative transfer model used does not yet allow for cloud/rain effects, it is crucial to properly identify and exclude (or correct) cloud/rain-contaminated radiances using quality control (QC) and bias correction procedures. The assimilation technique is unique in that both procedures take into account the effect of the liquid cloud on the difference between observed and simulated brightness temperature for each SSM/I channel. The estimate of the total column cloud liquid water from observed radiances is used in a frequency-dependent cloud detection component of the QC and as a predictor in the bias correction algorithm. Also, a microwave emissivity Jacobian model with respect to wind speed is developed for oceanic radiances. It was found that the surface wind information in the radiance data can be extracted through the emissivity model Jacobian rather than producing and including a separate SSM/I wind speed retrieval. A two-month-long data assimilation experiment from July to August 2004 using NCEP’s Gridpoint Statistical Interpolation analysis system and the NCEP operational forecast model was performed. In general, the assimilation of SSM/I radiance has a significant positive impact on the analyses and forecasts. Moisture is added in the Northern Hemisphere and Tropics and is slightly reduced in the Southern Hemisphere. The moisture added appears to be slightly excessive in the Tropics verified against rawinsonde observations. Nevertheless, the assimilation of SSM/I radiance data reduces model spinup of precipitation and substantially improves the dynamic fields, especially in measures of the vector wind error at 200 hPa in the Tropics. In terms of hurricane tracks, SSM/I radiance assimilation produces more cases with smaller errors and reduces the average error. No disruption of the Hadley circulation is found from the introduction of the SSM/I radiance data. [ABSTRACT FROM AUTHOR]

This study focuses on improving the retrieval of rain from measured microwave brightness temperatures and the capability of the retrieved field to represent the mesoscale structure of a small intense hurricane. For this study, a database is constructed from collocated Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the TRMM Microwave Imager (TMI) data resulting in about 50 000 brightness temperature vectors associated with their corresponding rain-rate profiles. The database is then divided in two: a retrieval database of about 35 000 rain profiles and a test database of about 25 000 rain profiles. Although in principle this approach is used to build a database over both land and ocean, the results presented here are only given for ocean surfaces, for which the conditions for the retrieval are optimal. An algorithm is built using the retrieval database. This algorithm is then used on the test database, and results show that the error can be constrained to reasonable levels for most of the observed rain ranges. The relative error is nonetheless sensitive to the rain rate, with maximum errors at the low and high ends of the rain intensities (+60% and -30%, respectively) and a minimum error between 1 and 7 mm h-1. The retrieval method is optimized to exhibit a low total bias for climatological purposes and thus shows a high standard deviation on point-to-point comparisons. The algorithm is applied to the case of Hurricane Bret (1999). The retrieved rain field is analyzed in terms of structure and intensity and is then compared with the TRMM PR original rain field. The results show that the mesoscale structures are indeed well reproduced even if the retrieved rain misses the highest peaks of precipitation. Nevertheless, the mesoscale asymmetries are well reproduced and the maximum rain is found in the correct quadrant. Once again, the total bias is low, which allows for future calculation of the heat sources/sinks associated with precipitation production and evaporation. [ABSTRACT FROM AUTHOR]

Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar–radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation–brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation–brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal components and the brightness temperature principal components of the classes. Because the classes are stratified by the sea surface temperature and the echo-top-height estimator, the number of classes that are considered for retrieval is significantly smaller than the total number of classes, making the algorithm computationally efficient. The radiometer-only algorithm is applied to TMI observations, and precipitation estimates are compared with combined TRMM precipitation radar (PR)–TMI reference estimates. The TMI-only algorithm, supported by the empirically derived database, produces estimates that are more consistent with the reference values than the precipitation estimates from the version-6 TRMM facility TMI algorithm. Cloud-resolving model simulations are used to assign a latent heating profile to each precipitation profile in the empirically derived database, making it possible to estimate latent heating using the radiometer-only algorithm. Although the evaluation of latent heating estimates in this study is preliminary, because realistic conditional probability distribution functions are attached to latent heating structures in the algorithm's database, a generally positive impact on latent heating estimation from passive microwave observations is expected. [ABSTRACT FROM AUTHOR]

The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature cutoff was used, but this is modified to a polarization-corrected temperature criterion. These modifications are made for the TRMM version 6 product release and the third (1 September) release of AMSR-E products to the public, both in 2004. The modifications are slightly different for each of these two sensors. [ABSTRACT FROM AUTHOR]

Moisture fields from the NCEP–DOE reanalysis-2 (R-2) and Television Infrared Observational Satellite (TIROS) Operational Vertical Sounder (TOVS) Pathfinder A are validated using the Special Sensor Microwave Imager (SSM/I) retrievals over the Southern Ocean. It is shown that the spatial distributions of the annual mean statistics of the total precipitable water are similar among SSM/I, R-2, and TOVS Pathfinder A for both the eddy and mean components. However, transient statistics show that the R-2 total precipitable water agrees with SSM/I with a correlation of 0.77 over the Southern Ocean while the TOVS Pathfinder A moisture is almost uncorrelated with the SSM/I data. Total moisture transport convergence for 1988 over the Antarctic continent is further examined using the R-2 wind and moisture data as well as the moisture retrievals from TOVS Pathfinder A. To gain a better understanding of transient and mean processes on moisture transport, the total moisture transport was decomposed into mean and eddy components. The results suggest that a mass conservation correction is necessary for the mean component, but can safely be ignored for the eddy component. With the mass conservation correction, the mean moisture transport is about the same for both the R-2 estimate alone and the estimate based on the mixed TOVS Pathfinder A moisture–R-2 wind. The computed eddy and total moisture transport convergence over Antarctica for the R-2 data agrees within 10%–15% with previous surface-data-based estimates as well as estimates from other model analyses. However, the eddy component of the mixed TOVS moisture with R-2 wind is about 60%–70% lower than the R-2 result. These differences occur because the eddy moisture amplitude of the TOVS Pathfinder A is nearly 40% lower than the R-2 data and also because the TOVS moisture has a much lower correlation with the R-2 winds. These results reflect the difficulties with the TOVS sensor in quantifying synoptic moisture transients resulting from conditional sampling problems. [ABSTRACT FROM AUTHOR]

A detailed evaluation of snow water equivalent (SWE) and snow cover extent (SCE) derived using the combined Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) brightness temperature records for the 1978–2002 period was carried out for a longitudinal transect in the continental interior of North America. Comparison with in situ SWE observations showed that the SMMR brightness temperature adjustments are required to produce SWE retrievals with similar bias and rmse as observed during the SSM/I period. Underestimation of SCE in the passive microwave dataset (relative to NOAA snow charts) was identified as a systematic problem, most pronounced in early winter and during seasons with above-average snow extent. The passive microwave data were successfully merged with historical data based on strong interdataset agreement for a 1978–92 overlap period. Analysis of SWE and SCE time series for the months of December through March 1915–2002 provided information on SWE and SCE variability over the interior of North America, but yielded no evidence of significant trends in either variable over the 88-yr period. Anomalies observed during the relatively recent period of passive microwave data acquisition did not exceed the range of anomalies observed in the historical data record. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) with the capability of measuring sea surface temperature (SST) in the presence of clouds, has been providing an unprecedented view of tropical basin-scale SST variability. In this paper, an assessment of the accuracy of the SST derived from TMI over the Bay of Bengal using in situ data collected from moored buoys and research ships, is presented. The authors find that TMI captures the evolution of the SST of the bay on seasonal time scales with reasonable accuracy. The mean difference between the SST from TMI and buoys is less than 0.1°C, and the rms difference is about 0.6°C. The time scales of the intraseasonal variation of the TMI SST are realistic. However, the amplitude of the SST variation on the intraseasonal scale is overestimated by a factor of about 1.3 when compared to buoy data. It is observed that the SST derived from TMI tends to be lower during periods with deep convection or winds stronger than 10 m s-1, or both. There is better agreement during weak conditions of convection/wind. This leads to a cold bias during convectively active periods when running average SST time series are constructed from SSTs retrieved from the TMI. [ABSTRACT FROM AUTHOR]

TRMM microwave imager rain estimates are used to quantify the spatial distribution of rainfall in tropical cyclones (TCs) over the global oceans. A total of 260 TCs were observed worldwide from 1 January 1998–31 December 2000, providing 2121 instantaneous TC precipitation observations. To examine the relationship between the storm intensity, its geographical location, and the rainfall distribution, the dataset is stratified into three intensity groups and six oceanic basins. The three intensity classes used in this study are tropical storms (TSs) with winds <33 m s-1, category 1–2 hurricane-strength systems (CAT12) with winds from 34–48 m s-1, and category 3–5 systems (CAT35) with winds >49 m s-1. The axisymmetric component of the TC rainfall is represented by the radial distribution of the azimuthal mean rainfall rates (R). The mean rainfall distribution is computed using 10-km annuli from the storm center to a 500-km radius. The azimuthal mean rain rates vary with storm intensity and from basin to basin. The maximum R is about 12 mm h-1 for CAT35, but decreases to 7 mm h-1 for CAT12, and to 3 mm h-1 for TS. The radius from the storm center of the maximum rainfall decreases with increasing storm intensity, from 50 km for TS to 35 km for CAT35 systems. The asymmetric component is determined by the first-order Fourier decomposition in a coordinate system relative to the storm motion. The asymmetry in TC rainfall varies significantly with both storm intensity and geographic locations. For the global average of all TCs, the maximum rainfall is located in the front quadrants. The location of the maximum rainfall shifts from the front-left quadrant for TS to the front-right for CAT35. The amplitude of the asymmetry varies with intensity as well; TS shows a larger asymmetry than CAT12 and CAT35. These global TC rainfall distributions and variability observed in various ocean basins should help to improve TC rainfall forecasting worldwide. [ABSTRACT FROM AUTHOR]

Mesoscale models are often used to explicitly predict discrete, highly structured phenomena. Information regarding the ability of the model to predict events as coherent entities is thus a useful statement of performance. Observational constraints are a significant problem, though, as the shape, size, and intensity of any given event are often only partially known. Composite techniques offer an attractive approach because the full deterministic information about any one event need not be known. If enough quasi-random observations of a distribution of events exist, bulk properties of the distributions of forecasts and observations can be estimated. Composites are also useful in that the verification measures are based on conditional samples of events. Sample distributions contingent on event existence in either the forecasts or the observations can be compared to one another. A verification technique in which meteorological events are located and composited on a relative grid centered on each event is described herein. This technique is described and demonstrated by comparing the 27-km Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) mistral wind forecasts to the Special Sensor Microwave Imager (SSM/I) observations for a 1-yr period. Diagnostic information regarding the forecast reliability, error type, and error spatial characteristics are derived. Also, statistics from the conditional distributions of both the observed and predicted events are compared. The difference between the two conditional biases (CBD) is found to reveal valuable information regarding the contribution of false alarms and missed forecasts to the forecast errors. The results indicate the mistral is remarkably predictable with high pattern correlations out to 66 h. [ABSTRACT FROM AUTHOR]

This paper reports the results from a regional validation study of monthly precipitation products generated from sensor measurements aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The study analyzed 4 yr of precipitation estimates (1998–2001) produced from data collected by the TRMM Microwave Imager (TMI) and the precipitation radar (PR) and compared them with corresponding estimates computed using 5-min rain accumulations from 66 rain gauges in the Oklahoma Mesonet. The applied methodology estimated bulk climate-scale sampling and retrieval errors and biases for the TMI and PR at two areal resolutions: 1° × 1° and 2° × 5°. The approach taken in this study generated two gauge-inferred gridded estimates of monthly precipitation over the study period: 1) G[sub 0] , which was computed by performing a complete integration of the monthly gauge time series, and 2) G[sub S] , which consisted of gauge-inferred rain rates subsampled to TRMM overpasses at a gridded resolution of 1° × 1°, with monthly precipitation estimates derived from the bulk statistics collected during each month of the study. The variable G[sub S] depends on the areal swath of each sensor and so yields two sensor-dependent estimates of gauge-inferred precipitation (G[sub TMI] , G[sub PR] ). The advantage of this approach is that it allows for the separation of retrieval and sampling errors, because the subsampled gauge estimates include the temporal sampling errors associated with the satellite sampling. The overall random sampling and retrieval errors for the PR exceeded the TMI errors for the study period, but the PR showed a greater reduction in the errors when the scale was increased to 2° × 5°. Annual coefficients of variation were also generally lower for the PR than the TMI at this scale. This result was consistent with PR retrieval biases, which were positive over all 4 yr, exceeding the TMI biases in every year of the study. [ABSTRACT FROM AUTHOR]

This study examines a mix of seven statistical and physical Special Sensor Microwave Imager (SSM/I) passive microwave algorithms that were designed for retrieval of over-ocean precipitable water (PW). The aim is to understand and explain why the algorithms exhibit a range of discrepancies with respect to measured PWs and with respect to each other, particularly systematic regional discrepancies that would produce substantive uncertainties in water vapor transports and radiative cooling in the context of climate dynamics. Data analysis is used to explore the nature of the algorithm differences, while radiative transfer analysis is used to explore the influence of several environmental variables (referred to as tangential environmental factors) that affect the PW retrievals. These are sea surface temperature (SST), surface wind speed (U[sub s] ), cloud liquid water path (LWP), and vertical profile structure of water vapor [q(z)]. The main datasets include the Wentz matched radiosonde–SSM/I point database consisting of 42 months of globally distributed oceanic radiosonde profiles paired with coincident SSM/I brightness temperatures, and globally compiled instantaneous orbit-swath maps of SSM/I brightness temperatures for January and July 1990. Results demonstrate that the seemingly good agreement found in past studies and herein, within the conventional framework of scatter diagram analysis that ignores regional classification, gives way to poor agreement in the framework of monthly and zonally averaged differences. It is shown how much of the disagreement inherent to statistical algorithms is due to disjoint training datasets used in deriving algorithm regression coefficients. The investigation also explores how tangential environmental factors composed of variations in SST, U[sub s] , cloud LWP, and q(z) structure impart dissimilar errors to retrieved PWs, according to the design of the retrieval algorithms. A discussion on implications of the discrepancies vis-à-vis the Global Energy and Water Cycle Experiment program is given, with suggestions on mitigating discrepancies in algorithm designs. [ABSTRACT FROM AUTHOR]