scholarly journals Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent Data

2006 ◽  
Vol 45 (5) ◽  
pp. 721-739 ◽  
Author(s):  
Song Yang ◽  
William S. Olson ◽  
Jian-Jian Wang ◽  
Thomas L. Bell ◽  
Eric A. Smith ◽  
...  
Keyword(s):  
Rain Rate ◽  
Sampling Error ◽  
Contextual Information ◽  
Total Error ◽  
Bias Reduction ◽  
Passive Microwave ◽  
Latent Heating ◽  
Cloud Resolving Model ◽  
Surface Rain Rate ◽  
Heating Profiles

Abstract Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5°-resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r ∼0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5°-resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5°-resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5°-resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.

Impact of Cloud Cover on Solar Radiative Biases in Deep Convective Regimes

2005 ◽  
Vol 62 (6) ◽  
pp. 1989-2000 ◽  
Author(s):  
F. Di Giuseppe ◽  
A. M. Tompkins
Keyword(s):  
Zenith Angle ◽  
Cloud Cover ◽  
Solar Zenith Angle ◽  
Total Error ◽  
Three Dimensional ◽  
Bias Reduction ◽  
Total Cloud Cover ◽  
Correction Technique ◽  
Cloud Resolving Model ◽  
Path Direction

Abstract Conflicting claims have been made concerning the magnitude of the bias in solar radiative transfer calculations when horizontal photon transport is neglected for deep convective scenarios. The difficulty of obtaining a realistic set of cloud scenes for situations of complex cloud geometry, while certain characteristics such as total cloud cover are systematically controlled, has hindered the attempt to reach a consensus. Here, a simple alternative approach is adopted. An ensemble of cloud scenes generated by a cloud resolving model are modified by an idealized function that progressively alters the cirrus anvil coverage without affecting the realism of the scene produced. Comparing three-dimensional radiative calculations with the independent column approximation for all cloud scenes, it is found that the bias in scene albedo can reach as much as 22% when the sun is overhead and 46% at low sun angles. The bias is an asymmetrical function of cloud cover with a maximum attained at cirrus anvil cloud cover of approximately 30%–40%. With a cloud cover of 15%, the bias is half its maximum value, while it is limited for coverage exceeding 80%. The position of the peak occurs at the cloud cover coinciding with the maximum number of independent clouds present in the scene. Increasing the cloud cover past this point produces a decrease in the number of isolated clouds because of cloud merging, with a consequential bias reduction. With this systematic documentation of the biases as a function of total cloud cover, it is possible to identify two contributions to the total error: the geometrical consequences of the effective cloud cover increase at low sun angles and the true 3D scattering effect of photons deviating from the original path direction. An attempt to account for the former geometrical contribution to the 1D bias is made by performing a simple correction technique, whereby the field is sheared by the tangent of the solar zenith angle. It is found that this greatly reduces the 1D biases at low sun angles. Because of the small aspect ratio of the cirrus cloud deck, the remaining bias contribution is small in magnitude and almost independent of solar zenith angle.

Spectral Retrieval of Latent Heating Profiles from TRMM PR Data. Part IV: Comparisons of Lookup Tables from Two- and Three-Dimensional Cloud-Resolving Model Simulations

2009 ◽  
Vol 22 (20) ◽  
pp. 5577-5594 ◽  
Author(s):  
Shoichi Shige ◽  
Yukari N. Takayabu ◽  
Satoshi Kida ◽  
Wei-Kuo Tao ◽  
Xiping Zeng ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Latent Heating ◽  
3D Simulations ◽  
Toga Coare ◽  
Trmm Pr ◽  
Lookup Tables ◽  
Cloud Resolving Model ◽  
Heating Profiles ◽  
Maximum Heating ◽  
Melting Level

Abstract The spectral latent heating (SLH) algorithm was developed to estimate latent heating profiles for the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR). The method uses TRMM PR information (precipitation-top height, precipitation rates at the surface and melting level, and rain type) to select heating profiles from lookup tables (LUTs). LUTs for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) using a cloud-resolving model (CRM). The two-dimensional (2D) CRM was used in previous studies. The availability of exponentially increasing computer capabilities has resulted in three-dimensional (3D) CRM simulations for multiday periods becoming increasingly prevalent. In this study, LUTs from the 2D and 3D simulations are compared. Using the LUTs from 3D simulations results in less agreement between the SLH-retrieved heating and sounding-based heating for the South China Sea Monsoon Experiment (SCSMEX). The level of SLH-estimated maximum heating is lower than that of the sounding-derived maximum heating. This is explained by the fact that using the 3D LUTs results in stronger convective heating and weaker stratiform heating above the melting level than is the case if using the 2D LUTs. More condensate is generated in and carried from the convective region in the 3D model than in the 2D model, and less condensate is produced by the stratiform region’s own upward motion.

scholarly journals 1997/98 El Niño–Induced Changes in Rainfall Vertical Structure in the East Pacific

2011 ◽  
Vol 24 (24) ◽  
pp. 6373-6391 ◽  
Author(s):  
Rui Li ◽  
Qilong Min ◽  
Yunfei Fu
Keyword(s):  
El Niño ◽  
Large Scale ◽  
Vertical Structure ◽  
Rain Rate ◽  
El Nino ◽  
Latent Heating ◽  
East Pacific ◽  
Surface Rain Rate ◽  
Induced Changes ◽  
The Impact

Abstract The 1997/98 El Niño–induced changes in rainfall vertical structure in the east Pacific (EP) are investigated by using collocated Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and associated daily SST and 6-hourly reanalysis data during January, February, March, and April of 1998, 1999, and 2000. This study shows that there are five key parameters, that is, surface rain rate, precipitation-top height (or temperature), and precipitation growth rates at upper, middle, and low layers to define a rainfall profile, and those five key parameters are strongly influenced by both SST and large-scale dynamics. Under the influence of 1997/98 El Niño, the precipitation-top heights in the EP were systematically higher by about 1 km than those under non–El Niño conditions, while the freezing level was about 0.5 km higher. Under the constraints of rain type, surface rain rate, and the precipitation top, the shape of rainfall profile still showed significant differences: the rain growth was relatively faster in the mid-layer (−5° to +2°C isotherm) but slower in the lower layer (below +2°C isotherm) under the influence of El Niño. It is also evident that the dependence of precipitation top height on SST was stronger under large-scale decent (non–El Niño) circulations but much weaker under large-scale ascent (El Niño) circulations. The combined effect of larger vertical extent and greater growth rate in the middle layer further shifted latent heating upward as compared with the impact of horizontal changes in the rain type fractions (convective versus stratiform). Such additional latent heating shift would certainly further elevate circulation centers and strengthen the upper-layer circulation.

scholarly journals Improved Passive Microwave Retrievals of Rain Rate over Land and Ocean. Part I: Algorithm Description

2013 ◽  
Vol 30 (11) ◽  
pp. 2493-2508 ◽  
Author(s):  
Grant W. Petty ◽  
Ke Li
Keyword(s):  
Dimensional Reduction ◽  
Rain Rate ◽  
A Priori ◽  
Companion Paper ◽  
Passive Microwave ◽  
Effective Density ◽  
Retrieval Algorithm ◽  
Current Standard ◽  
Surface Rain Rate ◽  
Microwave Imager

Abstract A new approach to passive microwave retrievals of precipitation is described that relies on an objective dimensional reduction procedure to filter, normalize, and decorrelate geophysical background noise while retaining the majority of radiometric information concerning precipitation. The dimensional reduction also sharply increases the effective density of any a priori database used in a Bayesian retrieval scheme. The method is applied to passive microwave data from the Tropical Rainfall Measuring Mission (TRMM), reducing the original nine channels to three “pseudochannels” that are relatively insensitive to most background variations occurring within each of seven surface classes (one ocean plus six land and coast) for which they are defined. These pseudochannels may be used in any retrieval algorithm, including the current standard Goddard profiling algorithm (GPROF), in place of the original channels. The same methods are also under development for the Global Precipitation Measurement (GPM) Core Observatory Microwave Imager (GMI). Starting with the pseudochannel definitions, a new Bayesian algorithm for retrieving the surface rain rate is described. The algorithm uses an a priori database populated with matchups between the TRMM precipitation radar (PR) and the TRMM Microwave Imager (TMI). The explicit goal of the algorithm is to retrieve the PR-derived best estimate of the surface rain rate in portions of the TMI swath not covered by the PR. A unique feature of the new algorithm is that it provides robust posterior Bayesian probabilities of pixel-averaged rain rate exceeding various thresholds. Validation and intercomparison of the new algorithm is the subject of a companion paper.

Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA)

2008 ◽  
Vol 47 (3) ◽  
pp. 778-794 ◽  
Author(s):  
K. A. Hilburn ◽  
F. J. Wentz
Keyword(s):  
Spatial Patterns ◽  
Liquid Water ◽  
Rain Rate ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Global Precipitation Climatology Project ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Surface Rain Rate ◽  
Microwave Imager

Abstract The Unified Microwave Ocean Retrieval Algorithm (UMORA) simultaneously retrieves sea surface temperature, surface wind speed, columnar water vapor, columnar cloud water, and surface rain rate from a variety of passive microwave radiometers including the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The rain component of UMORA explicitly parameterizes the three physical processes governing passive microwave rain retrievals: the beamfilling effect, cloud and rainwater partitioning, and effective rain layer thickness. Rain retrievals from the previous version of UMORA disagreed among different sensors and were too high in the tropics. These issues have been fixed with more realistic rain column heights and proper modeling of saturation and footprint-resolution effects in the beamfilling correction. The purpose of this paper is to describe the rain algorithm and its recent improvements and to compare UMORA retrievals with Goddard Profiling Algorithm (GPROF) and Global Precipitation Climatology Project (GPCP) rain rates. On average, TMI retrievals from UMORA agree well with GPROF; however, large differences become apparent when the instantaneous retrievals are compared on a pixel-to-pixel basis. The differences are due to fundamental algorithm differences. For example, UMORA generally retrieves higher total liquid water, but GPROF retrieves a higher surface rain rate for a given amount of total liquid water because of differences in microphysical assumptions. Comparison of UMORA SSM/I retrievals with GPCP shows similar spatial patterns, but GPCP has higher global averages because of greater amounts of precipitation in the extratropics. UMORA and GPCP have similar linear trends over the period 1988–2005 with similar spatial patterns.

scholarly journals Spectral Retrieval of Latent Heating Profiles from TRMM PR Data. Part III: Estimating Apparent Moisture Sink Profiles over Tropical Oceans

2008 ◽  
Vol 47 (2) ◽  
pp. 620-640 ◽  
Author(s):  
Shoichi Shige ◽  
Yukari N. Takayabu ◽  
Wei-Kuo Tao
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Latent Heating ◽  
Tropical Ocean ◽  
Tropical Oceans ◽  
Cloud Resolving Model ◽  
Trmm Precipitation ◽  
Low Levels ◽  
Heating Profiles ◽  
The Western Pacific ◽  
Good Agreement

Abstract The spectral latent heating (SLH) algorithm was developed to estimate apparent heat source (Q1) profiles for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Parts I and II of this study. In this paper, the SLH algorithm is used to estimate apparent moisture sink (Q2) profiles. The procedure of Q2 retrieval is the same as that of heating retrieval except for using the Q2 profile lookup tables derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) utilizing a cloud-resolving model (CRM). The Q2 profiles were reconstructed from CRM-simulated parameters with the COARE table and then compared with CRM-simulated “true” Q2 profiles, which were computed directly from the water vapor equation in the model. The consistency check indicates that discrepancies between the SLH-reconstructed and CRM-simulated profiles for Q2, especially at low levels, are larger than those for Q1 and are attributable to moistening for the nonprecipitating region that SLH cannot reconstruct. Nevertheless, the SLH-reconstructed total Q2 profiles are in good agreement with the CRM-simulated ones. The SLH algorithm was applied to PR data, and the results were compared with Q2 profiles derived from the budget study. Although discrepancies between the SLH-retrieved and sounding-based profiles for Q2 for the South China Sea Monsoon Experiment (SCSMEX) are larger than those for heating, key features of the vertical profiles agree well. The SLH algorithm can also estimate differences of Q2 between the western Pacific Ocean and the Atlantic Ocean, consistent with the results from the budget study.

scholarly journals Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part I: Improved Method and Uncertainties

2006 ◽  
Vol 45 (5) ◽  
pp. 702-720 ◽  
Author(s):  
William S. Olson ◽  
Christian D. Kummerow ◽  
Song Yang ◽  
Grant W. Petty ◽  
Wei-Kuo Tao ◽  
...  
Keyword(s):  
Random Error ◽  
Rain Rate ◽  
Passive Microwave ◽  
Latent Heating ◽  
Sampling Errors ◽  
Heating Rates ◽  
Model Simulations ◽  
Temporal Sampling ◽  
Radiative Model ◽  
Rain Rates

Abstract A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

scholarly journals Equatorial Waves in the Upper Troposphere and Lower Stratosphere Forced by Latent Heating Estimated from TRMM Rain Rates

2011 ◽  
Vol 68 (10) ◽  
pp. 2321-2342 ◽  
Author(s):  
Jung-Hee Ryu ◽  
M. Joan Alexander ◽  
David A. Ortland
Keyword(s):  
Rain Rate ◽  
Lower Stratosphere ◽  
Equatorial Waves ◽  
Latent Heating ◽  
Upper Troposphere ◽  
Equivalent Depth ◽  
Equatorial Wave ◽  
Spectral Peaks ◽  
Heating Profiles ◽  
Rain Rates

Abstract Equatorial atmospheric waves in the upper troposphere and lower stratosphere (UTLS), excited by latent heating, are investigated by using a global spectral model. The latent heating profiles are derived from the 3-hourly Tropical Rainfall Measuring Mission (TRMM) rain rates, which include both convective- and stratiform-type profiles. The type of heating profile is determined based on an intensity of the surface rain rate. Latent heating profiles over stratiform rain regions, estimated from the TRMM Precipitation Radar (PR) product, are applied to derive the stratiform-type latent heating profiles from the gridded rain rate data. Monthly zonal-mean latent heating profiles derived from the rain rates appear to be reasonably comparable with the TRMM convective/stratiform heating product. A broad spectrum of Kelvin, mixed Rossby–gravity (MRG), equatorial Rossby (ER), and inertia–gravity waves are generated in the model. Particularly, equatorial waves (Kelvin, ER, and MRG waves) of zonal wavenumbers 1–5 appear to be dominant in the UTLS. In the wavenumber–frequency domain, the equatorial waves have prominent spectral peaks in the range of 12–200 m of the equivalent depth, while the spectral peaks of the equatorial waves having shallower equivalent depth (<50 m) increase in the case where stratiform-type heating is included. These results imply that the stratiform-type heating might be relevant for the shallower equivalent depth of the observed convectively coupled equatorial waves. The horizontal and vertical structures of the simulated equatorial waves (Kelvin, ER, and MRG waves) are in a good agreement with the equatorial wave theory and observed wave structure. In particular, comparisons of the simulated Kelvin waves and the High Resolution Dynamics Limb Sounder (HIRDLS) satellite observation are discussed.

scholarly journals Changes in Temperature and Precipitation Extremes in Superparameterized CAM in Response to Warmer SSTs

2017 ◽  
Vol 30 (24) ◽  
pp. 9827-9845 ◽  
Author(s):  
Xin Zhou ◽  
Marat F. Khairoutdinov
Keyword(s):  
Large Scale ◽  
Rain Rate ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Global Scale ◽  
Precipitation Extremes ◽  
Temperature And Precipitation ◽  
Hourly Data ◽  
Cloud Resolving Model ◽  
Cold Extremes

Subdaily temperature and precipitation extremes in response to warmer SSTs are investigated on a global scale using the superparameterized (SP) Community Atmosphere Model (CAM), in which a cloud-resolving model is embedded in each CAM grid column to simulate convection explicitly. Two 10-yr simulations have been performed using present climatological sea surface temperature (SST) and perturbed SST climatology derived from the representative concentration pathway 8.5 (RCP8.5) scenario. Compared with the conventional CAM, SP-CAM simulates colder temperatures and more realistic intensity distribution of precipitation, especially for heavy precipitation. The temperature and precipitation extremes have been defined by the 99th percentile of the 3-hourly data. For temperature, the changes in the warm and cold extremes are generally consistent between CAM and SP-CAM, with larger changes in warm extremes at low latitudes and larger changes in cold extremes at mid-to-high latitudes. For precipitation, CAM predicts a uniform increase of frequency of precipitation extremes regardless of the rain rate, while SP-CAM predicts a monotonic increase of frequency with increasing rain rate and larger change of intensity for heavier precipitation. The changes in 3-hourly and daily temperature extremes are found to be similar; however, the 3-hourly precipitation extremes have a significantly larger change than daily extremes. The Clausius–Clapeyron scaling is found to be a relatively good predictor of zonally averaged changes in precipitation extremes over midlatitudes but not as good over the tropics and subtropics. The changes in precipitable water and large-scale vertical velocity are equally important to explain the changes in precipitation extremes.

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