Abstract

Multispectral, high-resolution imagery from the Advanced Very High Resolution Radiometer of NOAA polar orbiting satellites is used to analyze the cloud-top structure of convective storms that develop a cirrus feature above the anvil, referred to as a plume, whose origin remains unclear. Images from the radiometer's channels 2, 3, and 4 and a combination of any two of these suggest a relationship between the emergence of such plumes and a source of small ice particles (diameter around 3.7 µm, channel 3 wavelength) at the cloud top. Unique observations of deep convective storms over Europe are presented and discussed. The paper does not provide an exhaustive explanation of the phenomenon but contributes original material to the study of convective storm cloud-top structure, which is far from being completely described.

Abstract

No Abstract available.

The transition from the Advanced Very High Resolution Radiometer (AVHRR)/2 to AVHRR/3 on NOAA polar orbiters was associated with a switching from daylight operations of the 3.7- to 1.6μm wave band, while retaining 3.7 μm for nighttime operations. Investigations of the daylight applicability of the two channels suggest that the 1.6-μm wave band for daylight operations does not prove to be the better choice, at least for cloud applications. The 3.7-μm wave band is much less affected by surface contamination, and measures more faithfully and unambiguously the particle effective radius near cloud tops. The 1.6-μm radiation penetrates deeper into the cloud, supplying an integrated signal throughout the inner portions of the cloud, including surface contribution. Therefore, a synergetic use of the two wave bands can provide an improved retrieval of cloud microstructure and precipitation than from any of the channels alone. However, when one channel must be selected for the AVHRR/3, 3.7 μm performs much better for these applications. Both wave bands identify equally well microphysical features in the anvils of severe storms. For other applications, such as detection of ice and snow over vegetated surfaces and desert dust aerosols, the 1.6-μm wave band does not present clear advantages with respect to 3.7 μm, except that it can be used directly as is, whereas the 3.7-μm wave band has to be corrected for the thermal emission and water vapor absorption. Anyway, the Moderate Resolution Imaging Spectroradiometer (MODIS) can be used instead for the applications to the relatively slowly changing surface properties, while prioritizing the AVHRR for the faster varying atmospheric applications. Finally, the 3.7-mm wave band is more effective in detecting fog, fires, and hot spots. All these factors need to be considered by the operators of AVHRR/3 making a justifiable choice of the channels for the maximum benefit of the user community.

Abstract

Long-term statistics of organized convection are vital to improved understanding of the hydrologic cycle at various scales. Satellite observations are used to understand the timing, duration, and frequency of deep convection in equatorial Africa, a region with some of the most intense thunderstorms. Yet little has been published about the propagation characteristics of mesoscale convection in that region. Diurnal, subseasonal, and seasonal cycles of cold cloud (proxy for convective precipitation) are examined on a continental scale. Organized deep convection consists of coherent structures that are characteristic of systems propagating under a broad range of atmospheric conditions. Convection is triggered by heating of elevated terrain, sea/land breezes, and lake breezes. Coherent episodes of convection result from regeneration of convection through multiple diurnal cycles while propagating westward. They have an average 17.6-h duration and 673-km span; most have zonal phase speeds of 8–16 m s⁻¹.

Propagating convection occurs in the presence of moderate low-level shear that is associated with the southwesterly monsoonal flow and midlevel easterly jets. Convection is also modulated by eastward-moving equatorially trapped Kelvin waves, which have phase speeds of 12–22 m s⁻¹ over equatorial Africa. Westward propagation of mesoscale convection is interrupted by the dry phase of convectively coupled Kelvin waves. During the wet phase, daily initiation and westward propagation continues within the Kelvin wave and the cold cloud shields are larger. Mesoscale convection is more widespread during the active phase of the Madden–Julian oscillation (MJO) but with limited westward propagation. The study highlights multiscale interaction as a major source of variability in convective precipitation during the critical rainy seasons in equatorial Africa.

Abstract

A method based on the synergistic use of low earth orbit (LEO) and geostationary earth orbit (GEO) satellite data for aerosol-type characterization, as well as aerosol optical thickness (AOT) retrieval and monitoring over the ocean, is presented. These properties are used for the estimation of the direct shortwave aerosol radiative forcing at the top of the atmosphere. The synergy serves the purpose of monitoring aerosol events at the GEO time and space scales while maintaining the accuracy level achieved with LEO instruments. Aerosol optical properties representative of the atmospheric conditions are obtained from the inversion of high-spectral-resolution measurements from the Global Ozone Monitoring Experiment (GOME). The aerosol optical properties are input for radiative transfer calculations for the retrieval of the AOT from GEO visible broadband measurements, avoiding the use of fixed aerosol models available in the literature. The retrieved effective aerosol optical properties represent an essential component for the aerosol radiative forcing assessment. A sensitivity analysis is also presented to quantify the effects that changes on the aerosol model may have on modeled results of spectral reflectance, AOT, and direct shortwave aerosol radiative forcing at the top of the atmosphere. The impact on modeled values of the physical assumptions on surface reflectance and vertical profiles of ozone and water vapor are analyzed. Results show that the aerosol model is the main factor influencing the investigated radiative variables. Results of the application of the method to several significant aerosol events, as well as their validation, are presented in a companion paper.

Abstract

A method based on the synergistic use of low earth orbit and geostationary earth orbit satellite data for aerosol-type characterization and aerosol optical thickness (AOT: τ _a ) retrieval and monitoring over the ocean is presented in Part I of this paper. The method is now applied to a strong dust outbreak over the Atlantic Ocean in June 1997 and to two other relevant transport events of biomass burning and desert dust aerosol that occurred in 2000 over the Atlantic and Indian Oceans, respectively. The retrievals of the aerosol optical properties are checked against retrievals from sun and sky radiance measurements from the ground-based Aerosol Robotic Network (AERONET). The single-scattering albedo values obtained from AERONET are always within the error bars presented for Global Ozone Monitoring Experiment (GOME) retrievals, resulting in differences lower than 0.041. The retrieved AOT values are compared with the independent space–time-collocated measurements from the AERONET, as well as to the satellite aerosol official products of the Polarization and Directionality of the Earth Reflectances (POLDER) and the Moderate Resolution Imaging Spectroradiometer (MODIS). A first estimate of the AOT accuracy derived from comparisons with AERONET data leads to ±0.02 ± 0.22τ _a when all AOT values are retained or to ±0.02 ± 0.16τ _a for aerosol transport events (AOT > 0.4). The upwelling flux at the top of the atmosphere (TOA) was computed with radiative transfer calculations and used to estimate the TOA direct shortwave aerosol radiative forcing; a comparison with space–time-collocated measurements from the Clouds and the Earth's Radiant Energy System (CERES) TOA flux product was also done. It was found that more than 90% of the values differ from CERES fluxes by less than ±15%.

Abstract

The purpose of this paper is to evaluate a new operational procedure to produce half-hourly rainfall estimates at 0.1° spatial resolution. Rainfall is estimated using a neural networks (NN)–based approach utilizing passive microwave (PMW) and infrared satellite measurements. Several neural networks are tested, from multilayer perceptron to adaptative resonance theory architectures. The NN analytical selection process is explained. Half- hourly rain gauge data over Andalusia, Spain, are used for validation purposes. Several interpolation procedures are tested to transform point to areal measurements, including the maximum entropy estimation method. Rainfall estimations are also compared with Geostationary Operational Environmental Satellite precipitation index and histogram-matching results. Half-hourly rainfall estimates give ∼0.6 correlations with PMW data (∼0.2 with gauge), and average correlations of up to 0.7 and 0.6 are obtained for 0.5° and 0.1° monthly accumulated estimates, respectively.

Abstract

Two heavy rain events over the Central Mediterranean basin, which are markedly different by genesis, dimensions, duration, and intensity, are analyzed. Given the relative low frequency of this type of severe storms in the area, a synoptic analysis describing their development is included. A multispectral analysis based on geostationary multifrequency satellite images is applied to identify cloud type, hydrometeor phase, and cloud vertical extension. Precipitation intensity is retrieved from (i) surface rain gauges, (ii) satellite data, and (iii) numerical model simulations. The satellite precipitation retrieval algorithm 183-Water vapor Strong Lines (183-WSL) is used to retrieve rain rates and cloud hydrometeor type, classify stratiform and convective rainfall, and identify liquid water clouds and snow cover from the Advanced Microwave Sounding Unit-B (AMSU-B) sensor data. Rainfall intensity is also simulated with the Weather Research and Forecasting (WRF) numerical model over two nested domains with horizontal resolutions of 16 km (comparable to that of the satellite sensor AMSU-B) and 4 km. The statistical analysis of the comparison between satellite retrievals and model simulations demonstrates the skills of both methods for the identification of the main characteristics of the cloud systems with a suggested overall bias of the model toward very low rain intensities. WRF (in the version used for the experiment) seems to classify as low rain intensity regions those areas where the 183-WSL retrieves no precipitation while sensing a mixture of freshly nucleated cloud droplets and a large amount of water vapor; in these areas, especially adjacent to the rain clouds, large amounts of cloud liquid water are detected. The satellite method performs reasonably well in reproducing the wide range of gauge-detected precipitation intensities. A comparison of the 183-WSL retrievals with gauge measurements demonstrates the skills of the algorithm in discriminating between convective and stratiform precipitation using the scattering and absorption of radiation by the hydrometeors.

Abstract

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.

Abstract

Six satellite-based rainfall estimates (SRFE)—namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]—were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba–Shabelle, and Baro–Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-to-pixel, subcatchment, and river basin for the period 2003–06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs.