Abstract

A statistical correlation technique is applied to the retrieval of vertical moisture profiles under clear-skyconditions from down-looking radiometric measurements of atmospheric radiation at microwave wavelengths. For a given set of channels, the method selects the optimum radiometric channels for estimating water vapor at specific pressure levels between the surface and 300 mb. The water vapor mixing ratio at these pressure levels is then calculated from a linear combination of the selected channel brightness temperatures. To test its validity the algorithm was applied, in a numerical experiment, to fifty independent tropical radiosondes.The rms absolute deviation of the estimated moisture profiles from the actual profiles was comparable to that obtained using an iterative retrieval method reported earlier. The statistical method, however, requires several orders of magnitude less computer time than the iterative method; it is suitable for high speed processing of large amounts of data.

Abstract

We have applied a multiple linear regression technique to retrieve continuous sequences of atmospheric moisture profiles from a set of measured data. In this method the selection of an optimal subset of sensor channels plays a crucial role, in order to reduce the impact of data noise and redundancy. The data were obtained with a 4-channel remote-sensing microwave instrument carried aboard an aircraft. In contrast, most previously reported moisture profile retrievals from microwave radiometry have been used on simulated, discontinuous data. Although our data were obtained over a land surface with only a limited amount of correlative data, the retrievals were quite successful.

Abstract

Two years of tropical oceanic cloud observations are analyzed using the operational CloudSat cloud classification product and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Relationships are examined between cloud types, sea surface temperature (SST), and location during the CloudSat early morning and afternoon overpasses. Based on CloudSat and combined lidar–radar products, the maximum and minimum cloud fractions occur at SSTs near 303 and 299 K, respectively, corresponding to deep convective/detrained cloud populations and the transition from shallow to deep convection. For SSTs below approximately 301 K, low clouds (stratiform and stratocumulus) are dominant (cloud fraction between 0.15 and 0.37) whereas high clouds are dominant for SSTs greater than about 301 K (cloud fraction between 0.18 and 0.28). Consistent with previous studies, most tropical low clouds are associated with lower SSTs, with a strong inverse linear relationship between low cloud frequency and SST. For all cloud types except nimbostratus, stratus, and stratocumulus, a sharp increase in frequency of occurrence is observed for SSTs between 299 and 300.5 K, deduced as the onset of deeper convection. Peak fractions of high, deep convective, altostratus, and altocumulus clouds occur at SSTs close to 303 K, while cumulus clouds, which have lower tops, show a smooth cloud fractional peak about 2° cooler. Deep convective and other high cloud types decrease sharply above SSTs of 303 K, in accordance with previous work suggesting a narrow window of tropical deep convection. Finally, significant cloud frequency differences exist between CloudSat early morning and afternoon overpasses, suggesting a diurnal cycle of some cloud types, particularly stratocumulus, high, and deep convective clouds.

Abstract

The possibility of using remote sensing retrievals to estimate apparent water vapor sinks and heat sources is explored. The apparent water vapor sinks and heat sources are estimated from a combination of remote sensing, specific humidity, and temperature from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS) and wind fields from the National Aeronautics and Space Administration (NASA)’s Goddard Space Flight Center (GSFC)’s Modern Era Retrospective-Analysis for Research and Applications (MERRA). The intraseasonal oscillation (ISO) of the Indian summer monsoon is used as a test bed to evaluate the apparent water vapor sink and heat source. The ISO-related northward movement of the column-integrated apparent water vapor sink matches that of precipitation observed by the Tropical Rainfall Measuring Mission (TRMM) minus the MERRA surface evaporation, although the amplitude of the variation is underestimated by 50%. The diagnosed water vapor and heat budgets associated with convective events during various phases of the ISO agree with the moisture–convection feedback mechanism. The apparent heat source moves northward coherently with the apparent water vapor sink associated with the deep convective activity, which is consistent with the northward migration of the precipitation anomaly. The horizontal advection of water vapor and dynamical warming are strong north of the convective area, causing the northward movement of the convection by the destabilization of the atmosphere. The spatial distribution of the apparent heat source anomalies associated with different phases of the ISO is consistent with that of the diabatic heating anomalies from the trained heating (TRAIN Q1) dataset. Further diagnostics of the TRAIN Q1 heating anomalies indicate that the ISO in the apparent heat source is dominated by a variation in latent heating associated with the precipitation.

Abstract

Atmospheric rivers (ARs) are often associated with extreme precipitation, which can lead to flooding or alleviate droughts. A decade (2003–12) of landfalling ARs impacting the North American west coast (between 32.5° and 52.5°N) is collected to assess the skill of five commonly used satellite-based precipitation products [T3B42, T3B42 real-time (T3B42RT), CPC morphing technique (CMORPH), PERSIANN, and PERSIANN–Cloud Classification System (CCS)] in capturing ARs’ precipitation rate and pattern. AR detection was carried out using a database containing twice-daily satellite-based integrated water vapor composite observations. It was found that satellite products are more consistent over ocean than land and often significantly underestimate precipitation rate over land compared to ground observations. Incorrect detection of precipitation from IR-based methods is prevalent over snow and ice surfaces where microwave estimates often show underestimation or missing data. Bias adjustment using ground observation is found very effective to improve satellite products, but it also raises concern regarding near-real-time applicability of satellite products for ARs. The analysis using individual case studies (6–8 January and 13–14 October 2009) and an ensemble of AR events suggests that further advancement in capturing orographic precipitation and precipitation over cold and frozen surfaces is needed to more reliably quantify AR precipitation from space.

Abstract

This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua’s Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors’ estimate for 3-yr (2007–09) near-global (80°S–80°N) oceanic mean precipitation rate is ~2.94 mm day⁻¹. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68 mm day⁻¹) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82 mm day⁻¹). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCP and CMAP, especially in the Southern Hemisphere.

Abstract

The authors investigate if atmospheric water vapor from remote sensing retrievals obtained from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS) and the water vapor budget from the NASA Goddard Space Flight Center (GSFC) Modern Era Retrospective-analysis for Research and Applications (MERRA) are physically consistent with independently synthesized precipitation data from the Tropical Rainfall Measuring Mission (TRMM) or the Global Precipitation Climatology Project (GPCP) and evaporation data from the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF). The atmospheric total water vapor sink (Σ) is estimated from AIRS water vapor retrievals with MERRA winds (AIRS–MERRA Σ) as well as directly from the MERRA water vapor budget (MERRA–MERRA Σ). The global geographical distributions as well as the regional wavelet amplitude spectra of Σ are then compared with those of TRMM or GPCP precipitation minus GSSTF surface evaporation (TRMM–GSSTF and GPCP–GSSTF P − E, respectively). The AIRS–MERRA and MERRA–MERRA Σs reproduce the main large-scale patterns of global P − E, including the locations and variations of the ITCZ, summertime monsoons, and midlatitude storm tracks in both hemispheres. The spectra of regional temporal variations in Σ are generally consistent with those of observed P − E, including the annual and semiannual cycles, and intraseasonal variations. Both AIRS–MERRA and MERRA–MERRA Σs have smaller amplitudes for the intraseasonal variations over the tropical oceans. The MERRA P − E has spectra similar to that of MERRA–MERRA Σ in most of the regions except in tropical Africa. The averaged TRMM–GSSTF and GPCP–GSSTF P − E over the ocean are more negative compared to the AIRS–MERRA, MERRA–MERRA Σs, and MERRA P − E.

Abstract

Forecast observing system simulation experiments (OSSEs) are conducted to assess the potential impact of geostationary microwave (GeoMW) sounder observations on numerical weather prediction forecasts. A regional OSSE is conducted using a tropical cyclone (TC) case that is very similar to Hurricane Harvey (2017), as hurricanes are among the most devastating of weather-related natural disasters, and hurricane intensity continues to pose a significant challenge for numerical weather prediction. A global OSSE is conducted to assess the potential impact of a single GeoMW sounder centered over the continental United States versus two sounders positioned at the current locations of the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES) East and West. It is found that assimilation of GeoMW soundings result in better characterization of the TC environment, especially before and during intensification, which leads to significant improvements in forecasts of TC track and intensity. TC vertical structure (warm core thermal perturbation and horizontal wind distribution) is also substantially improved, as are the surface wind and precipitation extremes. In the global OSSE, assimilation of GeoMW soundings leads to slight improvement globally and significant improvement regionally, with regional impact equal to or greater than nearly all other observation types.

Abstract

The atmospheric moisture and temperature profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit on the NASA Aqua mission, in combination with the precipitation from the Tropical Rainfall Measuring Mission (TRMM), are employed to study the vertical moist thermodynamic structure and spatial–temporal evolution of the Madden–Julian oscillation (MJO). The AIRS data indicate that, in the Indian Ocean and western Pacific, the temperature anomaly exhibits a trimodal vertical structure: a warm (cold) anomaly in the free troposphere (800–250 hPa) and a cold (warm) anomaly near the tropopause (above 250 hPa) and in the lower troposphere (below 800 hPa) associated with enhanced (suppressed) convection. The AIRS moisture anomaly also shows markedly different vertical structures as a function of longitude and the strength of convection anomaly. Most significantly, the AIRS data demonstrate that, over the Indian Ocean and western Pacific, the enhanced (suppressed) convection is generally preceded in both time and space by a low-level warm and moist (cold and dry) anomaly and followed by a low-level cold and dry (warm and moist) anomaly.

The MJO vertical moist thermodynamic structure from the AIRS data is in general agreement, particularly in the free troposphere, with previous studies based on global reanalysis and limited radiosonde data. However, major differences in the lower-troposphere moisture and temperature structure between the AIRS observations and the NCEP reanalysis are found over the Indian and Pacific Oceans, where there are very few conventional data to constrain the reanalysis. Specifically, the anomalous lower-troposphere temperature structure is much less well defined in NCEP than in AIRS for the western Pacific, and even has the opposite sign anomalies compared to AIRS relative to the wet/dry phase of the MJO in the Indian Ocean. Moreover, there are well-defined eastward-tilting variations of moisture with height in AIRS over the central and eastern Pacific that are less well defined, and in some cases absent, in NCEP. In addition, the correlation between MJO-related midtropospheric water vapor anomalies and TRMM precipitation anomalies is considerably more robust in AIRS than in NCEP, especially over the Indian Ocean. Overall, the AIRS results are quite consistent with those predicted by the frictional Kelvin–Rossby wave/conditional instability of the second kind (CISK) theory for the MJO.

Abstract

This study uses 45 years of observational records from 517 historical surface weather stations over northern Eurasia to examine changing precipitation characteristics associated with increasing air temperatures. Results suggest that warming air temperatures over northern Eurasia have been accompanied by higher precipitation intensity but lower frequency and little change in annual precipitation total. An increase in daily precipitation intensity of around 1%–3% per each degree of air temperature increase is found for all seasons as long as a station’s seasonal mean air temperature is below about 15°–16°C. This threshold temperature may be location dependent. At temperatures above this threshold, precipitation intensity switches to decreasing with increasing air temperature, possibly related to decreasing water vapor associated with extreme high temperatures. Furthermore, the major atmospheric circulation of the Arctic Oscillation, Scandinavian pattern, east Atlantic–western Eurasian pattern, and polar–Eurasian pattern also have significant influences on precipitation intensity in winter, spring, and summer over certain areas of northern Eurasia.