Abstract

An evaluation of the version-5 precipitation radar (PR; algorithm 2A25) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; algorithm 2A12) rainfall products is performed across the Tropics in two ways: 1) by comparing long-term TRMM rainfall products with Global Precipitation Climatology Centre (GPCC) global rain gauge analyses and 2) by comparing the rainfall estimates from the PR and TMI on a rainfall feature-by-feature basis within the narrow swath of the PR using a 1-yr database of classified precipitation features (PFs). The former is done to evaluate the overall biases of the TMI and PR relative to “ground truth” to examine regional differences in the estimates; the latter allows a direct comparison of the estimates with the same sampling area, also identifying relative biases as a function of storm type. This study finds that the TMI overestimates rainfall in most of the deep Tropics and midlatitude warm seasons over land with respect to both the GPCC gauge analysis and the PR (which agrees well with the GPCC gauges in the deep Tropics globally), in agreement with past results. The PR is generally higher than the TMI in midlatitude cold seasons over land areas with gauges. The analysis by feature type reveals that the TMI overestimates relative to the PR are due to overestimates in mesoscale convective systems and in most features with 85-GHz polarization-corrected temperature of less than 250 K (i.e., with a significant optical depth of precipitation ice). The PR tended to be higher in PFs without an ice-scattering signature of less than 250 K. Normalized for a subset of features with a large rain volume (exceeding 10⁴ mm h⁻¹ km²) independent of the PF classification, features with TMI > PR in the Tropics tended to have a higher fraction of stratiform rainfall, higher IR cloud tops, more intense radar profiles and 85-GHz ice-scattering signatures, and larger rain areas, whereas the converse is generally true for features with PR > TMI. Subtropical-area PF bias characteristics tended not to have such a clear relationship (especially over the ocean), a result that is hypothesized to be due to the influence of more variable storm environments and the presence of frontal rain. Melting-layer effects in stratiform rain and a bias in the ice-scattering–rain relationship were linked to the TMI producing more rainfall than the PR. However, noting the distinct characteristic biases Tropics-wide by feature type, this study reveals that accounting for regime-dependent biases caused by the differing horizontal and vertical morphologies of precipitating systems may lead to a reduction in systematic relative biases in a microwave precipitation algorithm.

Abstract

Observational and modeling studies have revealed the relationships between convective–stratiform rain proportion and the vertical distributions of vertical motion, latent heating, and moistening in mesoscale convective systems. Therefore, remote sensing techniques that can be used to quantify the area coverage of convective or stratiform rainfall could provide useful information regarding the dynamic and thermodynamic processes in these systems. In the current study, two methods for deducing the area coverage of convective precipitation from satellite passive microwave radiometer measurements are combined to yield an improved method. If sufficient microwave scattering by ice-phase precipitation is detected, the method relies mainly on the degree of polarization in oblique-view, 85.5-GHz radiances to estimate the fraction of the radiometer footprint covered by convection. In situations where ice scattering is minimal, the method draws mostly on texture information in radiometer imagery at lower microwave frequencies to estimate the convective area fraction.

Based upon observations of 10 organized convective systems over ocean and nine systems over land, instantaneous, 0.5°-resolution estimates of convective area fraction from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared with nearly coincident estimates from the TRMM precipitation radar (PR). TMI convective area fraction estimates are low-biased relative to PR estimates, with TMI–PR correlation coefficients of 0.78 and 0.84 over ocean and land surfaces, respectively. TMI monthly average convective area percentages in the Tropics and subtropics from February 1998 are greatest along the intertropical convergence zone and in the continental regions of the Southern (summer) Hemisphere. Although convective area percentages from the TMI are systematically lower than those derived from the PR, the monthly patterns of convective coverage are similar. Systematic differences in TMI and PR convective area percentages do not show any clear correlation or anticorrelation with differences in retrieved rain depths, and so discrepancies between TRMM version-5 TMI- and PR-retrieved rain depths are likely due to other sensor or algorithmic differences.

Abstract

Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote sensing error and, especially in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that rms random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain gauge and radar data. This relationship is examined using Special Sensor Microwave Imager (SSM/I) satellite data obtained over the western equatorial Pacific during the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. Rms error inferred directly from SSM/I rainfall estimates is found to be larger than was predicted from surface data and to depend less on local rain rate than was predicted. Preliminary examination of Tropical Rainfall Measuring Mission (TRMM) microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be computed directly from the satellite data.

Abstract

The combination of active and passive microwave sensors on board the Tropical Rainfall Measuring Mission (TRMM) satellite have been used to construct observationally constrained databases of precipitation profiles for use in passive microwave rainfall retrieval algorithms over oceans. The method uses a very conservative approach that begins with the operational TRMM precipitation radar algorithm and adjusts its solution only as necessary to simultaneously match the radiometer observations. Where the TRMM precipitation radar (PR) indicates no rain, an optimal estimation procedure using TRMM Microwave Imager (TMI) radiances is used to retrieve nonraining parameters. The optimal estimation methodology ensures that the geophysical parameters are fully consistent with the observed radiances. Within raining fields of view, cloud-resolving model outputs are matched to the liquid and frozen hydrometeor profiles retrieved by the TRMM PR. The profiles constructed in this manner are subsequently used to compute brightness temperatures that are immediately compared to coincident observations from TMI. Adjustments are made to the rainwater and ice concentrations derived by PR in order to achieve agreement at 19 and 85 GHz, vertically polarized brightness temperatures at monthly time scales. The database is generated only in the central 11 pixels of the PR radar scan, and the rain adjustment is performed independently for distinct sea surface temperature (SST) and total precipitable water (TPW) values. Overall, the procedure increases PR rainfall by 4.2%, but the adjustment is not uniform across all SST and TPW regimes. Rainfall differences range from a minimum of −57% for SST of 293 K and TPW of 13 mm to a maximum of +53% for SST of 293 K and TPW of 45 mm. These biases are generally reproduced by a TMI retrieval algorithm that uses the observationally generated database. The algorithm increases rainfall by 5.0% over the PR solution with a minimum of −99% for SST of 293 K and TPW of 14 mm to a maximum of +11.8% for an SST of 294 K and TPW of 50 mm. Some differences are expected because of the algorithm mechanics.

Abstract

A satellite data analysis is performed to explore the Madden–Julian oscillation (MJO) focusing on the potential roles of the equatorial Rossby (ER) and Kelvin waves. Measurements from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Visible/Infrared Scanner (VIRS) are analyzed in the frequency–wavenumber domain to identify and ultimately filter primary low-frequency modes in the Tropics. The space–time spectrum of deep-storm fraction estimated by PR and VIRS exhibits notable Kelvin wave signals at wavenumbers 5–8, a distinct MJO peak at wavenumbers 1–7 and periods of about 40 days, and a signal corresponding to the ER wave. These modes are separately filtered to study the individual modes and possible relationship among them in the time–longitude space. In 10 cases analyzed here, an MJO event is often collocated with a group of consecutive Kelvin waves as well as an intruding ER wave accompanied with the occasional onset of a stationary convective phase. The spatial and temporal relationship between the MJO and Kelvin wave is clearly visible in a lag composite diagram, while the ubiquity of the ER wave leads to a less pronounced relation between the MJO and ER wave. A case study based on the Geostationary Meteorological Satellite (GMS) imagery together with associated dynamic field captures the substructure of the planetary-scale waves. A cross-correlation analysis confirms the MJO-related cycle that involves surface and atmospheric parameters such as sea surface temperature, water vapor, low clouds, shallow convection, and near-surface wind as proposed in past studies. The findings suggest the possibility that a sequence of convective events coupled with the linear waves may play a critical role in MJO propagation. An intraseasonal radiative–hydrological cycle inherent in the local thermodynamic conditions could be also a potential factor responsible for the MJO by loosely modulating the envelope of the entire propagation system.

Abstract

Previous research has shown that the temperature and precipitation variability in the Upper Colorado River basin (UCRB) is correlated with large-scale climate variability [i.e., El Niño–Southern Oscillation (ENSO) and Pacific decadal oscillation (PDO)]. But this correlation is not very strong, suggesting the need to look beyond the statistics. Looking at monthly contributions across the basin, results show that February is least sensitive to variability, and a wet October could be a good predictor for a wet season. A case study of a wet and a dry year (with similar ENSO/PDO conditions) shows that the occurrence of a few large accumulating events is what drives the seasonal variability, and these large events can happen under a variety of synoptic conditions. Looking at several physical factors that can impact the amount of accumulation in any given event, it is found that large accumulating events (>10 mm in one day) are associated with westerly winds at all levels, higher wind speeds at all levels, and greater amounts of total precipitable water. The most important difference between a large accumulating and small accumulating event is the presence of a strong (>4 m s⁻¹) low-level westerly wind. Because much more emphasis should be given to this more local feature, as opposed to large-scale variability, an accurate seasonal forecast for the basin is not producible at this time.

Abstract

Raindrop size distribution (DSD) retrievals from two years of data gathered by the Tropical Rainfall Measuring Mission (TRMM) satellite and processed with a combined radar–radiometer algorithm over the oceans equatorward of 35° are examined for relationships with variables describing properties of the vertical precipitation profile, mesoscale organization, and background environment. In general, higher freezing levels and relative humidities (tropical environments) are associated with smaller reflectivity-normalized median drop size (ϵ _DSD) than in the extratropics. Within the tropics, the smallest ϵ _DSD values are found in large, shallow convective systems where warm rain formation processes are thought to be predominant, whereas larger sizes are found in the stratiform regions of organized deep convection. In the extratropics, the largest ϵ _DSD values are found in the scattered convection that occurs when cold, dry continental air moves over the much warmer ocean after the passage of a cold front. These relationships are formally attributed to variables describing the large-scale environment, mesoscale organization, and profile characteristics via principal component (PC) analysis. The leading three PCs account for 23% of the variance in ϵ _DSD at the individual profile level and 45% of the variance in 1°-gridded mean values. The geographical distribution of ϵ _DSD is consistent with many of the observed regional reflectivity–rainfall (Z–R) relationships found in the literature as well as discrepancies between the TRMM radar-only and radiometer-only precipitation products. In particular, midlatitude and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Ocean intertropical convergence zone.

Abstract

Life cycles of deep convective raining systems are documented through use of a Lagrangian tracking algorithm applied to high-resolution Climate Prediction Center morphing technique (CMORPH) rainfall data, permitting collocation with related environmental ancillary fields and the International Satellite Cloud Climatology Project (ISCCP) cloud states (). System life cycles are described in terms of propagation speed, duration, and dominant cloud structures. Tracked systems are usually associated with the ISCCP weather state 1 (WS1) deep convection cloud state and an independent, microwave-based deep convective precipitation regime developed here. The distribution and characteristics of tracked systems are found to be similar between ocean basins in terms of system speed and duration, with westward-propagating systems predominant in every basin.

The effects that these systems have on environmental parameters are assessed, stratified according to their average propagation speed and by ocean basin. Regardless of system speed the net effect on the environment is similar, with the largest difference being how quickly changes occur, with net surface radiation decreasing about 150 W m⁻² and total precipitable water perturbed by 5–7 kg m⁻²; sea surface temperature (SST) drops 0.2°–0.3°C over 24 h, with system speed affecting how long SSTs remain depressed. The observed drop in SST is partly caused by the presence of widespread, optically thick clouds that greatly decrease the net surface radiative flux. Quick changes in SSTs caused by tracked systems are captured by buoys but not represented well in gridded SST products, as these regions remain largely under the precipitating cloud cover associated with these systems.

Abstract

Anomalies of precipitation, cloud, thermodynamic, and radiation variables are analyzed on the large spatial scale defined by the tropical oceans. In particular, relationships between the mean tropical oceanic precipitation anomaly and radiative anomalies are examined. It is found that tropical mean precipitation is well correlated with cloud properties and radiative fields. In particular, the tropical mean precipitation anomaly is positively correlated with the top of the atmosphere reflected shortwave anomaly and negatively correlated with the emitted longwave anomaly. The tropical mean relationships are found to primarily result from a coherent oscillation of precipitation and the area of high-level cloudiness. The correlations manifest themselves radiatively as a modest decrease in net downwelling radiation at the top of the atmosphere, and a redistribution of energy from the surface to the atmosphere through reduced solar radiation to the surface and decreased longwave emission to space. Integrated over the tropical oceanic domain, the anomalous atmospheric column radiative heating is found to be about 10% of the magnitude of the anomalous latent heating. The temporal signature of the radiative heating is observed in the column mean temperature that indicates a coherent phase-lagged oscillation between atmospheric stability and convection. These relationships are identified as a radiative–convective cloud feedback that is observed on intraseasonal time scales in the tropical atmosphere.

Abstract

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 TRMM precipitation. The results indicate that discrepancies are related to shifts from isolated deep convection during La Niña toward organized precipitation during El Niño with 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 in TMI 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 between PR and TMI estimates yields positive responses of precipitation to tropical SSTs of 10% °C⁻¹ and 17% °C⁻¹, respectively, consistent with 15% °C⁻¹ from the Global Precipitation Climatology Project (GPCP).