A major rain storm in Uttarakhand (India) caused heavy rains and major loss of life from floods and land slide during 16–18 June, 2013. The observed daily maximum rainfall rates (3-hourly) during the ...16th and 17th June were 220 and 340 mm respectively. This event is addressed via sensitivity studies using a cloud resolving non-hydrostatic model with detailed microphysics. The streaming of moist air from the east-south-east and warmer air from the south-west contributed to the sustained large population and amplitude of buoyancy and the associated CAPE contributed to the longer period of heavy rains. This study addresses the concept of Buoyancy as a means for large vertical accelerations, stronger vertical motions, extreme rain rates and the mechanisms that relate to the time rates of change. A post-processing algorithm provides an analysis of time rate of change for the buoyancy. Moist air streams and warm/moist air intrusions into heavily raining clouds are part of this buoyancy enhancement framework. Improvements in modeling of the extreme rain event came from adaptive observational strategy that showed lack of moisture data sets in these vital regions. We show that a moist boundary layer near the Bay of Bengal leads to moist rivers of moisture where the horizontal convergence confines a large population of buoyancy elements with large magnitudes of buoyancy that streams towards the region of extreme orographic rains. The areas covered in this study include: (i) Use of high resolution cloud modeling (1-km), (ii) Now casting of rains using physical initialization with a Newtonian relaxation, (iii) Use of an adaptive observational strategy, (iii) Sensitivity of the evolution of fields and population of buoyancy elements to boundary layer moisture, (iv) Role of orography and details of buoyancy budget.
A method for improving weather and climate forecast skill has been developed. It is called a superensemble, and it arose from a study of the statistical properties of a low-order spectral model. ...Multiple regression was used to determine coefficients from multimodel forecasts and observations. The coefficients were then used in the superensemble technique. The superensemble was shown to outperform all model forecasts for multiseasonal, medium-range weather and hurricane forecasts. In addition, the superensemble was shown to have higher skill than forecasts based solely on ensemble averaging.
Two major findings of this study are i) the presence of a robust intraseasonal oscillation (ISO) signal in the Bay of Bengal (BOB) subsurface ocean heat content (OHC) anomaly, this ISO signal ...propagates northward at the rate of roughly one degree latitude per day and ii) a lag of roughly 4 to 6days between the appearance of this signal in the surface wind speed and followed by the OHC anomaly. The availability of long-term daily data sets for the OHC made it possible to examine the presence of ISO over the BOB. This study also examines the possible relationship of ISO signal in the OHC with other parameters, such as surface wind (wind speed, wind stress curl and related upwelling/downwelling), sea surface temperature (SST), net surface heat flux into the ocean and surface rain. This study confirms the finding of a robust signal for the OHC anomaly over the BoB, on the time scale of ISO, using daily data sets for a 17years period covering each of the summer monsoon seasons. Also included is an analysis of rainfall and river runoff variability over north of the BoB. The river runoff and the precipitation data sets also show a robust ISO time scale. The mutual relationships among precipitation, SST, net heat flux and OHC are also examined in this study. This work addresses the lag relationship between the surface wind speed and the OHC, a lag of roughly 4days relates to strong winds impacting a cooling of the ocean. The ranges of spread of all the above parameters for the total field and for the ISO time scale are expressed as percentage variability of the ISO time scale. Those show that the OHC anomaly over the Bay of Bengal is quite a robust feature. This study does not address modeling issues.
•Meridionally propagating ISO time scale signal in the heat content anomaly of the upper ocean of the Bay of Bengal.•ISO time scale signal in a number of other parameters such as precipitation, winds, SST, net heat flux and river runoff.•Time lag among the ISO time scale variability in the heat content anomaly of the upper ocean and other parameters.•Importance of ocean heat content compared to SST on modulating the monsoon rainfall.
This study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge-based rainfall data covering the years 1987-2001, rain-rate initialization, four ...global atmosphere-ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of RMS error, anomaly correlation, equitable threat score, and ranked probability skill score, are (i) high forecast skills for the downscaled superensemble-based seasonal forecasts compared to the forecasts from the direct use of large-scale model forecasts were possible; (ii) very high scores for rainfall forecasts have been noted separately for dry and wet years, for different regions over India and especially for heavier rains in excess of 15 mm daysup -1; and (iii) the superensemble forecast skills exceed that of the benchmark observed climatology. The availability of reliable measures of high-resolution rain gauge-based rainfall was central for this study. Overall, the proposed algorithms, added together, show very promising results for the prediction of monsoon rains on the seasonal time scale. PUBLICATION ABSTRACT
Studies of Indian summer monsoon rainfall (ISMR: June–September) on regional scales are critically important for various applications related to agriculture and water management in India. Based on ...the coherent rainfall over regional scales, the India Meteorological Department defined four so‐called homogeneous regions: northwest India (NWI), northeast India (NEI), central India (CI), and south peninsula India (SPIN). Here we present the salient features of daily rainfall behavior over these four regions and their association with the strength of ISMRs between strong and weak monsoons. Our results reveal that rainfall rates are primarily stronger (weaker) over NWI, CI, and SPIN during strong (weak) monsoons, while rainfall rates over NEI show no remarkable changes between strong and weak monsoons. Specifically, stronger rainfalls over NWI, CI, and SPIN during strong monsoons are generally caused by prolonged and strong wet spells followed by short and weak dry spells, primarily during the mature phase and the withdrawal phase. In contrast, weaker rainfalls over NWI, CI, and SPIN during weak monsoons are mainly caused by the prolonged and strong dry spells, which mostly occur in July–August, followed by short wet spells. The distinctive rainfall features over each region are closely associated with the characteristics of regional convective activity over each region. The rainfall rates over NEI appear insensitive to the strength of ISMRs. Finally, a probability density function analysis indicates that the rainfall rates over the four homogeneous regions between strong and weak monsoons can be characterized by the likelihood of occurrence of different rainfall ranges.
Key Points
Rainfall rates over different zones are distinctive between strong and weak ISMRs
Rainfall rates over NEI show insensitivity to the strength of ISMRs
Greater likelihood of moderate versus light daily rainfall rate causes larger rainfall during strong versus weak monsoons
In this study, we examine the transitions in the monsoon phases (onset, active, break and the withdrawal) during an entire monsoon season. This makes use of a host of observational tools that come ...from GPM (Global Precipitation Measurement) and TRMM (Tropical Rainfall Measuring Mission) satellites for precipitation estimates, the vertical structure of rain, hydrometeors and cloud types from TRMM and CloudSat datasets. During onset, the mean moisture convergence, especially over west and south-west coast of India is 2 × 10
−4
kg m
−1
s
−1
; however, it carries much higher value of >4 × 10
−4
kg m
−1
s
−1
during the active phase over central eastern India. Much lesser moisture convergence (<1 × 10
−4
kg m
−1
s
−1
) is noted over Western Ghats area during the break phase. However, there are northeasterly moisture fluxes present over southern part of India during withdrawal phase. The tall cumulonimbus clouds that extend out to 16 km are illustrate during onset, the active phase is dominated by alto stratus and nimbostratus type clouds that are somewhat shallower. In general, we noted an absence of such clouds during the break and the withdrawal phases. Those structures were consistent in a number of derived fields such as the moisture convergence, moisture fluxes, the energy conversions between the rotational and the divergent kinetic energy and the corresponding phases of the intra-seasonal oscillations.
The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of ...a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the “forecast phase,” the forecasts from all of the member models use the downscaling coefficients of the “training phase.” The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble.
These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts.
The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) have enhanced the accuracy of rainfall estimation from satellites over ocean and land. An algorithm ...to merge TRMM Multi-satellite Precipitation Analysis (TMPA) satellite estimates with the India Meteorological Department (IMD) rain-gauge values is tested for the Indian monsoon region. A daily merged gauge and satellite data product (NMSG) at 1° latitude-longitude resolution for the Indian monsoon region is prepared to depict the large-scale aspects of monsoon rainfall. The satellite product used as a first guess is the TRMM TMPA for daily estimates. Incorporation of IMD gauge data corrects the mean biases of the TMPA values. TMPA alone is able to depict the space-time distribution of monsoon rainfall patterns. The merging of gauge data enhances the value of the satellite information; therefore, the NMSG is more representative than TMPA. Daily, monthly, and seasonal fields are prepared and compared with the land-only gridded data of the India Meteorological Department National Climate Centre (IMDNCC) at the same resolution. This inter-comparison with another independent dataset confirms the utility of the NMSG, produced by this objective analysis algorithm. The comparison of the merged data with the TMPA data reveals the regions where the satellite estimates have mean biases. Objective statistical scores also confirm the goodness of NMSG. The NMSG data are meant for use in verification of large-scale rainfall features from numerical models for the monsoon region.
The superensemble technique has previously been demonstrated to provide an improved seasonal forecast compared to the bias-removed ensemble of equally weighted models. This paper offers a further ...improvement to the superensemble method by modifying the regression coefficients used in the weighting of the models for the construction of the superensemble. The improvement is achieved by use of singular value decomposition of the covariance matrix, and selecting only the largest singular value, corresponding to maximal explained variance, for the calculation of the regression coefficients. The results shown here are based on calculations done with 10 yr worth of monthly forecasts from the Atmospheric Model Intercomparison Project (AMIP) dataset, using cross validation.
Abstract
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.
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