Abstract

The authors quantify systematic differences between modern observation- and reanalysis-based estimates of atmospheric heating rates and identify dominant variability modes over tropical oceans. Convergence of heat fluxes between the top of the atmosphere and the surface are calculated over the oceans using satellite-based radiative and sensible heat fluxes and latent heating from precipitation estimates. The convergence is then compared with column-integrated atmospheric heating based on Tropical Rainfall Measuring Mission data as well as the heating calculated using temperatures from the Atmospheric Infrared Sounder and wind fields from the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Corresponding calculations using MERRA and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis heating rates and heat fluxes are also performed. The geographical patterns of atmospheric heating rates show heating regimes over the intertropical convergence zone and summertime monsoons and cooling regimes over subsidence areas in the subtropical oceans. Compared to observation-based datasets, the reanalyses have larger atmospheric heating rates in heating regimes and smaller cooling rates in cooling regimes. For the averaged heating rates over the oceans in 40°S–40°N, the observation-based datasets have net atmospheric cooling rates (from −15 to −22 W m⁻²) compared to the reanalyses net warming rates (5.0–5.2 W m⁻²). This discrepancy implies different pictures of atmospheric heat transport. Wavelet spectra of atmospheric heating rates show distinct maxima of variability in annual, semiannual, and/or intraseasonal time scales. In regimes where deep convection frequently occurs, variability is mainly driven by latent heating. In the subtropical subsidence areas, variability in radiative heating is comparable to that in latent heating.

Abstract

In this study, satellite passive microwave sensor observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q ₁ − Q_R ) where Q ₁ is the apparent heat source and Q_R is the radiative heating rate in regions of precipitation. The TMI heating algorithm (herein called TRAIN) is calibrated or “trained” using relatively accurate estimates of heating based on spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based on a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically integrated condensation and surface precipitation.

Estimates of Q ₁ − Q_R from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q ₁ produced by combining TMI Q ₁ − Q_R with independently derived estimates of Q_R show reasonable agreement with rawinsonde-based analyses of Q ₁ from two field campaigns, although the satellite estimates exhibit heating profile structures with sharper and more intense heating peaks than the rawinsonde estimates.

Abstract

The total (i.e., convective and stratiform) latent heat release (LHR) cycle in the eyewall region of Hurricane Opal (October 1995) has been estimated using observations from the F-10, F-11, and F-13 Defense Meteorological Satellite Program Special Sensor Microwave/Imagers (SSM/Is). This LHR cycle occurred during the hurricane’s rapid intensification and decay stages (3–5 October 1995). The satellite observations revealed that there were at least two major episodes in which a period of elevated total LHR (i.e., convective burst) occurred in the eyewall region. During these convective bursts, Opal’s minimum pressure decreased by 50 mb and the LHR generated by convective processes increased, as greater amounts of latent heating occurred at middle and upper levels. It is hypothesized that the abundant release of latent heat in Opal’s middle- and upper-tropospheric region during these convective burst episodes allowed Opal’s eyewall to become more buoyant, enhanced the generation of kinetic energy and, thereby, rapidly intensified the system. The observations also suggest that Opal’s intensity became more responsive to the convective burst episodes (i.e., shorter time lag between LHR and intensity and greater maximum wind increase) as Opal became more intense.

Analyses of SSM/I-retrieved parameters, sea surface temperature observations, and the European Centre for Medium-Range Weather Forecasts (ECMWF) data reveal that the convective rainband (CRB) cycles and sea surface and tropopause temperatures, in addition to large-scale environmental forcing, had a profound influence on Opal’s episodes of convective burst and its subsequent intensity. High sea surface (29.7°C) and low tropopause (192 K) temperatures apparently created a greater potential for Opal’s maximum intensity. Strong horizontal moisture flux convergence within Opal’s outer-core regions (i.e., outside 333-km radius from the center) appeared to help initiate and maintain Opal’s CRBs. These CRBs, in turn, propagated inward to help generate and dissipate the eyewall convective bursts. The first CRB that propagated into Opal’s eyewall region appeared to initiate the first eyewall convective burst. The second CRB propagated to within 111 km of Opal’s center and appeared to dissipate the first CRB, subjecting it to subsidence and the loss of water vapor flux. The ECMWF upper-tropospheric height and wind analyses suggest that Opal interacted with a diffluent trough that initated an outflow channel, and generated high values of upper-tropospheric eddy relative angular momentum flux convergence. The gradient wind adjustment processes associated with Opal’s outflow channel, in turn, may have helped to initiate and maintain the eyewall convective bursts. The ECMWF analyses also suggest that a dry air intrusion within the southwestern quadrant of Opal’s outer-core region, together with strong vertical wind shear, subsequently terminated Opal’s CRB cycle and caused Opal to weaken prior to landfall.

Abstract

This study seeks to evaluate the impact of several newly available sources of meteorological data on mesoscale model forecasts of the extratropical cyclone that struck Florida on 2 February 1998. Intermittent measurements of precipitation and integrated water vapor (IWV) distributions were obtained from Special Sensor Microwave/Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations. The TMI also provided sea surface temperatures (SSTs) with structural detail of the Loop Current and Gulf Stream. Continuous lightning distributions were measured with a network of very low frequency radio receivers. Lightning data were tuned with intermittent spaceborne microwave radiometer data through a probability matching technique to continuously estimate convective rainfall rates.

A series of experiments were undertaken to evaluate the effect of those data on mesoscale model forecasts produced after assimilating processed rainfall and IWV for 6 h. Assimilating processed rainfall, IWV, and SSTs from TMI measurements in the model yielded improved forecasts of precipitation distributions and vertical motion fields. Assimilating those data also produced an improved 9-h forecast of the radar reflectivity cross section that was validated with a coincident observation from the TRMM spaceborne precipitation radar.

Sensitivity experiments showed that processed rainfall information had greater impact on the rainfall forecast than IWV and SST information. Assimilating latent heating in the correct location of the forecast model was found to be more important than an accurate determination of the rainfall intensity.

Abstract

This paper uses observations from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI) to evaluate the cloud microphysical schemes in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; version 3.7.4) for a wintertime frontal precipitation system over the eastern Pacific Ocean. By incorporating a forward radiative transfer model, the radar reflectivity and brightness temperatures are simulated and compared with the observations at PR and TMI frequencies. The main purpose of this study is to identify key differences among the five schemes [including Simple ice, Reisner1, Reisner2, Schultz, and Goddard Space Flight Center (GSFC) microphysics scheme] in the MM5 that may lead to significant departures of simulated precipitation properties from both active (PR) and passive (TMI) microwave observations. Radiative properties, including radar reflectivity, attenuation, and scattering in precipitation liquid and ice layers are investigated. In the rain layer, most schemes are capable of reproducing the observed radiative properties to a reasonable degree; the Reisner2 simulation, however, produces weaker reflectivity and stronger attenuation than the observations, which is possibly attributable to the larger intercept parameter (N _0r) applied in this run. In the precipitation ice layer, strong evidence regarding the differences in the microphysical and radiative properties between a narrow cold-frontal rainband (NCFR) and a wide cold-frontal rainband (WCFR) within this frontal precipitation system is found. The performances of these schemes vary significantly on simulating the microphysical and radiative properties of the frontal rainband. The GSFC scheme shows the least bias, while the Reisner1 scheme has the largest bias in the reflectivity comparison. It appears more challenging for the model to replicate the scattering signatures obtained by the passive sensor (TMI). Despite the common problem of excessive scattering in the WCFR (stratiform precipitation) region in every simulation, the magnitude of the scattering maximum seems better represented in the Reisner2 scheme. The different types of precipitation ice, snow, and graupel are found to behave differently in the relationship of scattering versus reflectivity. The determinative role of the precipitation ice particle size distribution (intercept parameters) is extensively discussed through sensitivity tests and a single-layer radiative transfer model.

Abstract

The one-dimensional, steady-state melting-layer model developed in Part I of this study is used to calculate both the microphysical and radiative properties of melting precipitation, based upon the computed concentrations of snow and graupel just above the freezing level at applicable horizontal grid points of three-dimensional cloud-resolving model simulations. The modified 3D distributions of precipitation properties serve as input to radiative transfer calculations of upwelling radiances and radar extinction/reflectivities at the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) frequencies, respectively. At the resolution of the cloud-resolving model grids (∼1 km), upwelling radiances generally increase if mixed-phase precipitation is included in the model atmosphere. The magnitude of the increase depends upon the optical thickness of the cloud and precipitation, as well as the scattering characteristics of the mixed-phase particles and ice-phase precipitation aloft. Over the set of cloud-resolving model simulations utilized in this study, maximum radiance increases of 43, 28, 18, and 10 K are simulated at 10.65, 19.35, 37.0, and 85.5 GHz, respectively. The impact of melting on TMI-measured radiances is determined not only by the physics of the melting particles but also by the horizontal extent of the melting precipitation, given that the lower-frequency channels have footprints that extend over tens of kilometers. At TMI resolution, the maximum radiance increases are 16, 15, 12, and 9 K at the same frequencies. Simulated PR extinction and reflectivities in the melting layer can increase dramatically if mixed-phase precipitation is included, a result consistent with previous studies. Maximum increases of 0.46 (−2 dB) in extinction optical depth and 5 dB in reflectivity are simulated based upon the set of cloud-resolving model simulations.

Abstract

A global analysis that optimally combines observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the Tropics. In this study it is demonstrated that assimilating precipitation and total precipitable water (TPW) derived from the Tropical Rainfall Measuring Mission Microwave Imager (TMI) can significantly improve the quality of global analysis. It is shown that assimilating the 6-h averaged TMI rainfall and TPW retrievals improves not only the hydrological cycle, but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). Notably, assimilating TMI rain rates improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the Tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Assimilating these data also improves the instantaneous wind and temperature fields in the analysis, leading to better short-range forecasts in the Tropics. Ensemble forecasts initialized with analyses incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day, better 500-hPa geopotential height forecasts up to 5 days, and better 200-hPa divergent winds up to 2 days. These results demonstrate the potential of using high quality spaceborne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications.

Abstract

Owing to its profound influences on global energy balance, accurate representation of low cloud variability in climate models is an urgent need for future climate projection. In the present study, marine low cloud variability on intraseasonal time scales is characterized, with a particular focus over the Pacific basin during boreal summer and its association with the dominant mode of tropical intraseasonal variability (TISV) over the eastern Pacific (EPAC) intertropical convergence zone (ITCZ). Analyses indicate that, when anomalous TISV convection is enhanced over the elongated EPAC ITCZ, reduction of low cloud fraction (LCF) is evident over a vast area of the central North Pacific. Subsequently, when the enhanced TISV convection migrates to the northern part of the EPAC warm pool, a “comma shaped” pattern of reduced LCF prevails over the subtropical North Pacific, along with a pronounced reduction of LCF present over the southeast Pacific (SEPAC). Further analyses indicate that surface latent heat fluxes and boundary heights induced by anomalous low-level circulation through temperature advection and changes of total wind speed, as well as midlevel vertical velocity associated with the EPAC TISV, could be the most prominent factors in regulating the intraseasonal variability of LCF over the North Pacific. For the SEPAC, temperature anomalies at the top of the boundary inversion layer between 850 and 800 hPa play a critical role in the local LCF intraseasonal variations. Results presented in this study provide not only improved understanding of variability of marine low clouds and the underlying physics, but also a prominent benchmark in constraining and evaluating the representation of low clouds in climate models.

Abstract

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

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

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

Abstract

This study discusses a rainfall algorithm utilizing six channels of microwave radiance data from the Nimbus-7 Scanning Multifrequency Microwave Radiometer. The algorithm is intended for short-term climate studies over the ocean at low latitudes. To find a set of functional relationships, rain ratios are regressed on brightness temperatures for each channel. Next, these functions are integrated over a class of rain-rate distributions to find relations between mean brightness temperatures and mean rain rates. This step accounts for beam filling. Finally, weights are obtained for combining the rain rates from the individual channels. The weights vary with the rain rates, so that the optimum combination of channels is always used. Results are stored in a database grid 1° latitude × 1° longitude by one month. To test the algorithm, three years (1979–81) of data from the Indian Ocean are processed. These show spatial patterns very similar to previous climatological studies and to expected seasonal variations as determined at climatological observing stations. In addition, the results are compared with another microwave algorithm and with an infrared threshold method substantially the same as the GOES precipitation index.