A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.

Diurnal-scale signatures of monsoon rainfall over the Indian region from TRMM satellite observations

One of the most important modes of summer season precipitation variability over the Indian region, the diurnal cycle, is studied using the Tropical Rainfall Measuring Mission 3-hourly, 0.25 degrees x 0.25 degrees 3B42 rainfall product for nine years (1999-2007). Most previous studies have provided an analysis of a single year or a few years of satellite-or station-based rainfall data. Our study aims to systematically analyze the statistical characteristics of the diurnal-scale signature of rainfall over the Indian and surrounding regions. Using harmonic analysis, we extract the signal corresponding to diurnal and subdiurnal variability. Subsequently, the 3-hourly time period or the octet of rainfall peak for this filtered signal, referred to as the ``peak octet,'' is estimated, with care taken to eliminate spurious peaks arising out of Gibbs oscillations. Our analysis suggests that over the Bay of Bengal, there are three distinct modes of the peak octet of diurnal rainfall corresponding to 1130, 1430, and 1730 Indian standard time (IST), from the north central to south bay. This finding could be seen to be consistent with southward propagation of the diurnal rainfall pattern reported by earlier studies. Over the Arabian Sea, there is a spatially coherent pattern in the mode of the peak octet (1430 IST), in a region where it rains for more than 30% of the time. In the equatorial Indian Ocean, while most of the western part shows a late night/early morning peak, the eastern part does not show a spatially coherent pattern in the mode of the peak octet owing to the occurrence of a ual maxima (early morng and early/late afternoon). The imalayan foothills were found to have a mode of peak octet corresponding to 0230 IST, whereas over the Burmese mountains and the Western Ghats (west coast of India) the rainfall peaks during late afternoon/early evening (1430-1730 IST). This implies that the phase of the diurnal cycle over inland orography (e. g., Himalayas) is significantly different from coastal orography (e. g., Western Ghats). We also find that over the Gangetic plains, the peak octet is around 1430 IST, a few hours earlier compared to the typical early evening maxima over land.

How good are the simulations of tropical SST-rainfall relationship by IPCC AR4 atmospheric and coupled models?

The failure of atmospheric general circulation models (AGCMs) forced by prescribed SST to simulate and predict the interannual variability of Indian/Asian monsoon has been widely attributed to their inability to reproduce the actual sea surface temperature (SST)-rainfall relationship in the warm Indo-Pacific oceans. This assessment is based on a comparison of the observed and simulated correlation between the rainfall and local SST. However, the observed SSTconvection/rainfall relationship is nonlinear and for this a linear measure such as the correlation is not an appropriate measure. We show that the SST-rainfall relationship simulated by atmospheric and coupled general circulation models in IPCC AR4 is nonlinear, as observed, and realistic over the tropical West Pacific (WPO) and the Indian Ocean (IO). The SST-rainfall pattern simulated by the coupled versions of these models is rather similar to that from the corresponding atmospheric one, except for a shift of the entire pattern to colder/warmer SSTs when there is a cold/warm bias in the coupled version.

Performance of WRF-Chem over Indian region: Comparison with measurements

The aerosol mass concentrations over several Indian regions have been simulated using the online chemistry transport model, WRF-Chem, for two distinct seasons of 2011, representing the pre-monsoon (May) and post-monsoon (October) periods during the Indo-US joint experiment `Ganges Valley Aerosol Experiment (GVAX)'. The simulated values were compared with concurrent measurements. It is found that the model systematically underestimates near-surface BC mass concentrations as well as columnar Aerosol Optical Depths (AODs) from the measurements. Examining this in the light of the model-simulated meteorological parameters, we notice the model overestimates both planetary boundary layer height (PBLH) and surface wind speeds, leading to deeper mixing and dispersion and hence lower surface concentrations of aerosols. Shortcoming in simulating rainfall pattern also has an impact through the scavenging effect. It also appears that the columnar AODs are influenced by the unrealistic emission scenarios in the model. Comparison with vertical profiles of BC obtained from aircraft-based measurements also shows a systematic underestimation by the model at all levels. It is seen that concentration of other aerosols, viz., dust and sea-salt are closely linked with meteorological conditions prevailing over the region. Dust is higher during pre-monsoon periods due to the prevalence of north-westerly winds that advect dust from deserts of west Asia into the Indo-Gangetic plain. Winds and rainfall influence sea-salt concentrations. Thus, the unrealistic simulation of wind and rainfall leads to model simulated dust and sea-salt also to deviate from the real values; which together with BC also causes underperformance of the model with regard to columnar AOD. It appears that for better simulations of aerosols over Indian region, the model needs an improvement in the simulation of the meteorology.

Prediction of Indian rainfall during the summer monsoon season on the basis of links with equatorial Pacific and Indian Ocean climate indices

Interannual variation of Indian summer monsoon rainfall (ISMR) is linked to El Nino-Southern oscillation (ENSO) as well as the Equatorial Indian Ocean oscillation (EQUINOO) with the link with the seasonal value of the ENSO index being stronger than that with the EQUINOO index. We show that the variation of a composite index determined through bivariate analysis, explains 54% of ISMR variance, suggesting a strong dependence of the skill of monsoon prediction on the skill of prediction of ENSO and EQUINOO. We explored the possibility of prediction of the Indian rainfall during the summer monsoon season on the basis of prior values of the indices. We find that such predictions are possible for July-September rainfall on the basis of June indices and for August-September rainfall based on the July indices. This will be a useful input for second and later stage forecasts made after the commencement of the monsoon season.

Indian Ocean sea surface salinity variations in a coupled model

The variability of the sea surface salinity (SSS) in the Indian Ocean is studied using a 100-year control simulation of the Community Climate System Model (CCSM 2.0). The monsoon-driven seasonal SSS pattern in the Indian Ocean, marked by low salinity in the east and high salinity in the west, is captured by the model. The model overestimates runoff int the Bay of Bengal due to higher rainfall over the Himalayan-Tibetan regions which drain into the Bay of Bengal through Ganga-Brahmaputra rivers. The outflow of low-salinity water from the Bay of Bengal is to strong in the model. Consequently, the model Indian Ocean SSS is about 1 less than that seen in the climatology. The seasonal Indian Ocean salt balance obtained from the model is consistent with the analysis from climatological data sets. During summer, the large freshwater input into the Bay of Bengal and its redistribution decide the spatial pattern of salinity tendency. During winter, horizontal advection is the dominant contributor to the tendency term. The interannual variability of the SSS in the Indian Ocean is about five times larger than that in coupled model simulations of the North Atlantic Ocean. Regions of large interannual standard deviations are located near river mouths in the Bay of Bengal and in the eastern equatorial Indian Ocean. Both freshwater input into the ocean and advection of this anomalous flux are responsible for the generation of these anomalies. The model simulates 20 significant Indian Ocean Dipole (IOD) events and during IOD years large salinity anomalies appear in the equatorial Indian Ocean. The anomalies exist as two zonal bands: negative salinity anomalies to the north of the equator and positive to the south. The SSS anomalies for the years in which IOD is not present and for ENSO years are much weaker than during IOD years. Significant interannual SSS anomalies appear in the Indian Ocean only during IOD years.

Variability of Indian summer monsoon rainfall in daily data from gauge and satellite

It has long been thought that tropical rainfall retrievals from satellites have large errors. Here we show, using a new daily 1 degree gridded rainfall data set based on about 1800 gauges from the India Meteorology Department (IMD), that modern satellite estimates are reasonably close to observed rainfall over the Indian monsoon region. Daily satellite rainfalls from the Global Precipitation Climatology Project (GPCP 1DD) and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are available since 1998. The high summer monsoon (June-September) rain over the Western Ghats and Himalayan foothills is captured in TMPA data. Away from hilly regions, the seasonal mean and intraseasonal variability of rainfall (averaged over regions of a few hundred kilometers linear dimension) from both satellite products are about 15% of observations. Satellite data generally underestimate both the mean and variability of rain, but the phase of intraseasonal variations is accurate. On synoptic timescales, TMPA gives reasonable depiction of the pattern and intensity of torrential rain from individual monsoon low-pressure systems and depressions. A pronounced biennial oscillation of seasonal total central India rain is seen in all three data sets, with GPCP 1DD being closest to IMD observations. The new satellite data are a promising resource for the study of tropical rainfall variability.

Shift of peak in summer monsoon rainfall over Korea and its association with El Nino-Southern Oscillation

The annual cycle of rainfall over the Korean Peninsula is marked by two peaks: one during July and the other during August. Since the mid-1970s, the maximum rainfall over the Korean Peninsula has shifted from July to August. This shift in rainfall peak was caused by a significant increase of August rainfall after the mid-1970s. The basic reason for this shift has been traced to a change in teleconnection between El Nino-Southern Oscillation (ENSO) and August rainfall. The relationship between August rainfall over Korea and ENSO changed from 1954-1975 (PI) to 1976-2002 (PII). The variability of August rainfall was significantly associated with sea surface temperature (SST) variation over the eastern equatorial Pacific during PI, but this relationship is absent during the PII period. In El Nino years during PI, low-level westerly and southerly wind anomalies are dominant around the East China Sea, which relates to strong August rainfall. In La Nina years during PI, easterly and northerly wind anomalies are dominant. During the PII period, however, westerly and southerly wind anomalies around the East China Sea were responsible for the high August rainfall over the East Asian region, even though La Nina SST conditions were in effect over the eastern Pacific.

Prediction of the Indian summer monsoon rainfall using a state-of-the-art coupled ocean-atmosphere model

A state-of-the-art model of the coupled ocean-atmosphere system, the climate forecast system (CFS), from the National Centres for Environmental Prediction (NCEP), USA, has been ported onto the PARAM Padma parallel computing system at the Centre for Development of Advanced Computing (CDAC), Bangalore and retrospective predictions for the summer monsoon (June-September) season of 2009 have been generated, using five initial conditions for the atmosphere and one initial condition for the ocean for May 2009. Whereas a large deficit in the Indian summer monsoon rainfall (ISMR; June-September) was experienced over the Indian region (with the all-India rainfall deficit by 22% of the average), the ensemble average prediction was for above-average rainfall during the summer monsoon. The retrospective predictions of ISMR with CFS from NCEP for 1981-2008 have been analysed. The retrospective predictions from NCEP for the summer monsoon of 1994 and that from CDAC for 2009 have been compared with the simulations for each of the seasons with the stand-alone atmospheric component of the model, the global forecast system (GFS), and observations. It has been shown that the simulation with GFS for 2009 showed deficit rainfall as observed. The large error in the prediction for the monsoon of 2009 can be attributed to a positive Indian Ocean Dipole event seen in the prediction from July onwards, which was not present in the observations. This suggests that the error could be reduced with improvement of the ocean model over the equatorial Indian Ocean.

Numerical Modelling of the Observed Diurnal Cycle of Indian Monsoon Rainfall

We have analysed the diurnal cycle of rainfall over the Indian region (10S-35N, 60E-100E) using both satellite and in-situ data, and found many interesting features associated with this fundamental, yet under-explored, mode of variability. Since there is a distinct and strong diurnal mode of variability associated with the Indian summer monsoon rainfall, we evaluate the ability of the Weather Research and Forecasting Model (WRF) to simulate the observed diurnal rainfall characteristics. The model (at 54km grid-spacing) is integrated for the month of July, 2006, since this period was particularly favourable for the study of diurnal cycle. We first evaluate the sensitivity of the model to the prescribed sea surface temperature (SST), by using two different SST datasets, namely, Final Analyses (FNL) and Real-time Global (RTG). It was found that with RTG SST the rainfall simulation over central India (CI) was significantly better than that with FNL. On the other hand, over the Bay of Bengal (BoB), rainfall simulated with FNL was marginally better than with RTG. However, the overall performance of RTG SST was found to be better than FNL, and hence it was used for further model simulations. Next, we investigated the role of the convective parameterization scheme on the simulation of diurnal cycle of rainfall. We found that the Kain-Fritsch (KF) scheme performs significantly better than Betts-Miller-Janjić (BMJ) and Grell-Devenyi schemes. We also studied the impact of other physical parameterizations, namely, microphysics, boundary layer, land surface, and the radiation parameterization, on the simulation of diurnal cycle of rainfall, and identified the “best” model configuration. We used this configuration of the “best” model to perform a sensitivity study on the role of various convective components used in the KF scheme. In particular, we studied the role of convective downdrafts, convective timescale, and feedback fraction, on the simulated diurnal cycle of rainfall. The “best” model simulations, in general, show a good agreement with observations. Specifically, (i) Over CI, the simulated diurnal rainfall peak is at 1430 IST, in comparison to the observed 1430-1730 IST peak; (ii) Over Western Ghats and Burmese mountains, the model simulates a diurnal rainfall peak at 1430 IST, as opposed to the observed peak of 1430-1730 IST; (iii) Over Sumatra, both model and observations show a diurnal peak at 1730 IST; (iv) The observed southward propagating diurnal rainfall bands over BoB are weakly simulated by WRF. Besides the diurnal cycle of rainfall, the mean spatial pattern of total rainfall and its partitioning between the convective and stratiform components, are also well simulated. The “best” model configuration was used to conduct two nested simulations with one-way, three-level nesting (54-18-6km) over CI and BoB. While, the 54km and 18km simulations were conducted for the whole of July, 2006, the 6km simulation was carried out for the period 18 - 24 July, 2006. The results of our coarse- and fine-scale numerical simulations of the diurnal cycle of monsoon rainfall will be discussed.

Predicting the extremes of Indian summer monsoon rainfall with coupled ocean-atmosphere models

An analysis of the retrospective predictions by seven coupled ocean atmosphere models from major forecasting centres of Europe and USA, aimed at assessing their ability in predicting the interannual variation of the Indian summer monsoon rainfall (ISMR), particularly the extremes (i.e. droughts and excess rainfall seasons) is presented in this article. On the whole, the skill in prediction of extremes is not bad since most of the models are able to predict the sign of the ISMR anomaly for a majority of the extremes. There is a remarkable coherence between the models in successes and failures of the predictions, with all the models generating loud false alarms for the normal monsoon season of 1997 and the excess monsoon season of 1983. It is well known that the El Nino and Southern Oscillation (ENSO) and the Equatorial Indian Ocean Oscillation (EQUINOO) play an important role in the interannual variation of ISMR and particularly the extremes. The prediction of the phases of these modes and their link with the monsoon has also been assessed. It is found that models are able to simulate ENSO-monsoon link realistically, whereas the EQUINOO-ISMR link is simulated realistically by only one model the ECMWF model. Furthermore, it is found that in most models this link is opposite to the observed, with the predicted ISMR being negatively (instead of positively) correlated with the rainfall over the western equatorial Indian Ocean and positively (instead of negatively) correlated with the rainfall over the eastern equatorial Indian Ocean. Analysis of the seasons for which the predictions of almost all the models have large errors has suggested the facets of ENSO and EQUINOO and the links with the monsoon that need to be improved for improving monsoon predictions by these models.

Yanai waves in the western equatorial Indian Ocean

Observations and models have shown the presence of intraseasonal fluctuations in 20-30-day and 10-20-day bands in the equatorial Indian Ocean west of 60 degrees E (WEIO). Their spatial and temporal structures characterize them as Yanai waves, which we label low-frequency (LFYW) and high-frequency (HFYW) Yanai waves, respectively. We explore the dynamics of these intraseasonal signals, using an ocean general circulation model (Modular Ocean Model) and a linear, continuously stratified model. Yanai waves are forced by the meridional wind tau(y) everywhere in the WEIO most strongly during the monsoon seasons. They are forced both directly in the interior ocean and by reflection of the interior response from the western boundary; interference between the interior and boundary responses results in a complex surface pattern that propagates eastward and has nodes. Yanai waves are also forced by instabilities primarily during June/July in a region offshore from the western boundary (52-55 degrees E). At that time, eddies, generated by barotropic instability of the Southern Gyre, are advected southward to the equator. There, they generate a westward-propagating, cross-equatorial flow field, v(eq), with a wave number/frequency spectrum that fits the dispersion relation of a number of Yanai waves, and these waves are efficiently excited. Typically, Yanai waves associated with several baroclinic modes are excited by both wind and eddy forcing; and typically, they superpose to create beams that carry energy vertically and eastward along ray paths. The same processes generate LFYWs and HFYWs, and hence, their responses are similar; differences are traceable to the property that HFYWs have longer wavelengths than LFYWs for each baroclinic mode.

Large scale features and assessment of spatial scale correspondence between TMPA and IMD rainfall datasets over Indian landmass

Daily rainfall datasets of 10 years (1998-2007) of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) version 6 and India Meteorological Department (IMD) gridded rain gauge have been compared over the Indian landmass, both in large and small spatial scales. On the larger spatial scale, the pattern correlation between the two datasets on daily scales during individual years of the study period is ranging from 0.4 to 0.7. The correlation improved significantly (similar to 0.9) when the study was confined to specific wet and dry spells each of about 5-8 days. Wavelet analysis of intraseasonal oscillations (ISO) of the southwest monsoon rainfall show the percentage contribution of the major two modes (30-50 days and 10-20 days), to be ranging respectively between similar to 30-40% and 5-10% for the various years. Analysis of inter-annual variability shows the satellite data to be underestimating seasonal rainfall by similar to 110 mm during southwest monsoon and overestimating by similar to 150 mm during northeast monsoon season. At high spatio-temporal scales, viz., 1 degrees x1 degrees grid, TMPA data do not correspond to ground truth. We have proposed here a new analysis procedure to assess the minimum spatial scale at which the two datasets are compatible with each other. This has been done by studying the contribution to total seasonal rainfall from different rainfall rate windows (at 1 mm intervals) on different spatial scales (at daily time scale). The compatibility spatial scale is seen to be beyond 5 degrees x5 degrees average spatial scale over the Indian landmass. This will help to decide the usability of TMPA products, if averaged at appropriate spatial scales, for specific process studies, e.g., cloud scale, meso scale or synoptic scale.

A note on new indices for the equatorial Indian Ocean oscillation

It is now well known that there is a strong association of the extremes of the Indian summer monsoon rainfall (ISMR) with the El Nio and southern oscillation (ENSO) and the Equatorial Indian Ocean Oscillation (EQUINOO), later being an east-west oscillation in convection anomaly over the equatorial Indian Ocean. So far, the index used for EQUINOO is EQWIN, which is based on the surface zonal wind over the central equatorial Indian Ocean. Since the most important attribute of EQUINOO is the oscillation in convection/precipitation, we believe that the indices based on convection or precipitation would be more appropriate. Continuous and reliable data on outgoing longwave radiation (OLR), and satellite derived precipitation are now available from 1979 onwards. Hence, in this paper, we introduce new indices for EQUINOO, based on the difference in the anomaly of OLR/precipitation between eastern and western parts of the equatorial Indian Ocean. We show that the strong association of extremes of the Indian summer monsoon with ENSO and EQUINOO is also seen when the new indices are used to represent EQUINOO.

Role of the cloud adjustment time scale in simulation of the interannual variability of Indian summer monsoon

The simulation of precipitation in a general circulation model relying on relaxed mass flux cumulus parameterization scheme is sensitive to cloud adjustment time scale (CATS). In this study, the frequency of the dominant intra-seasonal mode and interannual variability of Indian summer monsoon rainfall (ISMR) simulated by an atmospheric general circulation model is shown to be sensitive to the CATS. It has been shown that a longer CATS of about 5 h simulates the spatial distribution of the ISMR better. El Nio Southern Oscillation-ISMR relationship is also sensitive to CATS. The equatorial Indian Ocean rainfall and ISMR coupling is sensitive to CATS. Our study suggests that a careful choice of CATS is necessary for adequate simulation of spatial pattern as well as interannual variation of Indian summer monsoon precipitation.