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This study examines the impact of rain-rate initialization (RINIT), microphysical modifications, and cloud torques (in the context of angular momentum) on hurricane intensity forecasts using a mesoscale model [[the Advanced Research Weather Research and Forecasting model (ARW-WRF)]] at a cloud-resolving resolution of 2.7 km. The numerical simulations are performed in a triple-nested manner (25, 8.3, and 2.7 km) for Hurricane Dennis of 2005. Unless mentioned otherwise, all the results discussed are from the innermost grid with finest resolution (2.7 km). It is found that the model results obtained from the RINIT technique demonstrated robust improvement in hurricane structure, track, and intensity forecasts compared to the control experiment (CTRL; i.e., without RINIT). Thereafter, using RINIT initial conditions datasets three sensitive experiments are designed by modifying specific ice microphysical parameters (i.e., temperature-independent snow intercept parameter, doubling number of concentrations of ice, and ice crystal diameter) within the explicit parameterization scheme [[i.e., the WRF Single-Moment 6-class (WSM6)]]. It is shown that the experiment with enhanced ice mass concentration and temperature-independent snow intercept parameter produces the strongest and weakest storms, respectively. The results suggest that the distributions of hydrometeors are also impacted by the limited changes introduced in the microphysical scheme (e.g., the quantitative amount of snow drastically reduced to 0.1--0.2 g kg−−1 when the intercept parameter of snow is made independent of temperature). It is noted that the model holds ice at a warmer temperature for a longer time with a temperature-independent intercept parameter. These variations in hydrometeor distribution in the eyewall region of the storm affect diabatic heating and vertical velocity structure and modulated the storm intensity. However, irrespective of the microphysical changes the quantitative amount of graupel hydrometeors remained nearly unaffected. Finally, the indirect effect of microphysical modifications on storm intensity through angular momentum and cloud torques is examined. A formulation to predict the short-term changes in the storm intensity using a parcel segment angular momentum budget method is developed. These results serve to elucidate the indirect impact of microphysical modifications on tropical cyclone intensity changes through modulation in cloud torque magnitude. [ABSTRACT FROM AUTHOR]

This paper deals with seasonal climate forecasting using as many as 13 coupled ocean–atmosphere models. An analysis of the individual model, multimodel ensemble, and FSU synthetic superensemble (FSUSSE) climate forecasts was performed for monthly and seasonal forecasts of precipitation, sea surface temperature (SST) and surface air temperature over the Euro-Mediterranean region including the land areas of Europe, North Africa and the Near East, during the period 1989–2001. In the FSUSSE methodology, forecasts are obtained by a weighted combination of the individual coupled ocean–atmosphere model forecasts based on a training period. The set of 13 individual climate forecast models utilized in this research is comprised of the seven models in the European suite of DEMETER models, a suite of four Florida State University models, the Australian POAMA model and the NCAR CCM3 model. The FSUSSE forecasts of seasonal precipitation anomalies were found to have the lowest root mean square (RMS) errors in comparison to the models in the multimodel ensemble, and their ensemble mean. However, the anomaly correlation (AC) coefficient results for seasonal precipitation anomaly forecasts by the FSUSSE were less impressive. The equitable threat scores for the FSUSSE forecasts of seasonal precipitation were found to be better than the various models in the multimodel ensemble, but those scores for the forecasts of positive seasonal anomalies were found to be worse than most of the models in the multimodel ensemble. The FSUSSE seasonal forecasts for SST and surface air temperature for the season considered (winter) were found to be excellent for both AC coefficients and RMS errors in the forecast anomalies. [ABSTRACT FROM AUTHOR]

Our collective understanding of azimuthally asymmetric features within the coherent structure of a tropical cyclone (TC) continues to improve with the availability of more detailed observations and high-resolution model outputs. However, a precise understanding of how these asymmetries impact TC intensity changes is lacking. Prior attempts at investigating the asymmetric impacts follow a mean–eddy partitioning that condenses the effect of all the asymmetries into one term and fails to highlight the differences in the role of asymmetries at different scales. In this study, we present a novel energetics-based approach to analyze the asymmetric impacts at multiple length scales during periods of rapid intensity changes. Using model outputs of TCs under low and high shear, we compute the different energy pathways that enhance/suppress the growth of multiscale asymmetries in the wavenumber (WN) domain. We then compare and contrast the energetics of the mean-flow field (WN 0) with that of the persistent, coherent vortex-scale asymmetric structures (WNs 1 and 2) and the more local, transient, sub-vortex-scale asymmetries (WNs ≥ 3). We find in our case studies that the dominant mechanisms of growth/decay of the asymmetries are the baroclinic conversion from available potential to kinetic energy at individual scales of asymmetries and the transactions of kinetic energy between the asymmetries of various length scales, rather than the barotropic mean–eddy transactions as is typically assumed. Our case study analysis further shows that the growth/decay of asymmetries is largely independent of the mean. Certain aspects of eddy energetics can potentially serve as early-warning indicators of TC rapid intensity changes. [ABSTRACT FROM AUTHOR]

The Lidar Atmospheric Sensing Experiment (LASE) on board the NASA DC-8 measured high-resolution profiles of water vapor and aerosols, and cloud distributions in 14 flights over the eastern North Atlantic during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) field experiment. These measurements were used to study African easterly waves (AEWs), tropical cyclones (TCs), and the Saharan air layer (SAL). These LASE measurements represent the first simultaneous water vapor and aerosol lidar measurements to study the SAL and its interactions with AEWs and TCs. Three case studies were selected for detailed analysis: (i) a stratified SAL, with fine structure and layering (unlike a well-mixed SAL), (ii) a SAL with high relative humidity (RH), and (iii) an AEW surrounded by SAL dry air intrusions. Profile measurements of aerosol scattering ratios, aerosol extinction coefficients, aerosol optical thickness, water vapor mixing ratios, RH, and temperature are presented to illustrate their characteristics in the SAL, convection, and clear air regions. LASE extinction-to-backscatter ratios for the dust layers varied from 35 ± 5 to 45 ± 5 sr, well within the range of values determined by other lidar systems. LASE aerosol extinction and water vapor profiles are validated by comparison with onboard in situ aerosol measurements and GPS dropsonde water vapor soundings, respectively. An analysis of LASE data suggests that the SAL suppresses low-altitude convection. Midlevel convection associated with the AEW and transport are likely responsible for high water vapor content observed in the southern regions of the SAL on 20 August 2008. This interaction is responsible for the transfer of about 7 × 1015 J (or 8 × 103 J m−2) latent heat energy within a day to the SAL. Initial modeling studies that used LASE water vapor profiles show sensitivity to and improvements in model forecasts of an AEW. [ABSTRACT FROM AUTHOR]

In this paper, a multi-model ensemble approach with statistical correction for seasonal precipitation forecasts using a coupled DEMETER model data set is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability; consequently, the predictive skill of extended forecasts remains quite low. One of the approaches to the improvement of seasonal prediction is the empirical weighted multi-model ensemble, or superensemble, combination. In the superensemble approach, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The skill of a superensemble relies strongly on the past performance of the individual member models used in its construction. The algorithm proposed here involves empirical orthogonal function (EOF) filtering of the actual data set prior to the construction of a multi-model ensemble or superensemble as an alternative solution for seasonal prediction. This algorithm generates a new data set from the input multi-model data set by finding a consistent spatial pattern between the observed analysis and the individual model forecast. This procedure is a multiple linear regression problem in the EOF space. The newly generated EOF-filtered data set is then used as an input data set for the construction of a multi-model ensemble and superensemble. The skill of forecast anomalies is assessed using statistics of categorical forecast, spatial anomaly correlation and root mean square (RMS) errors. The various verifications show that the unbiased multi-model ensemble of DEMETER forecasts improves the prediction of spatial patterns (i.e. the anomaly correlation), but it shows poor skill in categorical forecast. Due to the removal of seasonal mean biases of the different models, the forecast errors of the bias-corrected multi-model ensemble and superensemble are already quite small. Based on the anomaly correlation and RMS measures, the forecasts produced by the proposed method slightly outperform the other conventional forecasts. [ABSTRACT FROM AUTHOR]

A system for objectively producing daily large-scale analysis of rainfall for the Indian region has been developed and tested by using only available real-time rain gauge data and quantitative precipitation estimates from INSAT-1D IR data. The system uses a successive correction method to produce the analysis on a regular latitude–longitude grid. Quantitative precipitation estimates from the Indian National Satellite System (INSAT) operational geostationary satellite, INSAT-1D, IR data are used as the initial guess in the objective analysis method. Accumulated 24-h (daily) rainfall analyses are prepared each day by merging satellite and rain gauge data. The characteristics of the output from this analysis system have been examined by comparing the accumulated monthly observed rainfall with other available independent widely used datasets from the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) analyses. The monthly data prepared from the daily analyses are also compared with the subjectively analyzed India Meteorological Department (IMD) monthly rainfall maps. This comparison suggests that even with only the available real-time data from INSAT and rain gauge, it is possible to construct a usable large-scale rainfall map on regular latitude–longitude grids. This analysis, which uses a higher resolution and more local rain gauge data, is able to produce realistic details of the Indian summer monsoon rainfall patterns. The magnitude and distribution of orographic rainfall near the west coast of India is very different from and more realistic compared to both the GPCP and CMAP patterns. Due to the higher spatial resolution of the analysis system, the regions of heavy and light rain are demarcated clearly over the Indian landmass. Over the oceanic regions of the Arabian Sea, Bay of Bengal, and the equatorial Indian Ocean, the agreement of the analyzed rainfall at the monthly timescale is quite good compared to the other two datasets. For NWP and other model verification of large-scale rainfall, this dataset will be useful. In the field of rainfall monitoring within weather and climate research, this technique will have real-time applications with data from current (METSAT) and future (INSAT-3A and INSAT-3D) Indian geostationary satellites. [ABSTRACT FROM AUTHOR]

A new comprehensive surface temperature data set for India is used to document changes in Indian temperature over seven decades, in order to examine the patterns and possible effects of global warming. The data set is subdivided into pre-monsoon, monsoon, and post-monsoon categories in order to study the temperature patterns in each of these periods. When the decade means in maximum, minimum and daily mean temperature for the 2000s are compared to those of the 1950s, a consistent pattern of warming is found over northwestern and southern India, and a pattern of cooling is seen in a broad zone anchored over northeastern India and extending southwestward across central India. These patterns are explained by the presence of a large region of anthropogenic brown haze over India and adjacent ocean regions. These aerosols absorb solar radiation, leading to warming of the haze layer over northeastern and central India and to cooling of the surface air beneath. The heated air rises and then sinks to the north and south of the haze region over northwestern and southern India, warming the air by compression as it sinks in those regions. The possible impact of these temperature patterns on Indian agriculture is considered.