Seasonal forecasts of monsoon at regional scales are critical for many applications but are rarely attempted as even the skill at all-India scale is not yet adequate. However, the conventional approach of evaluation of forecast skill for all-India seasonal monsoon rainfall implicitly assumes that the model performance is more or less spatially homogeneous. It is possible, however, that over a climatically diverse region (with large latitudinal extent), the model skill is dependent on geographical location. In particular, over land-locked regions with large orography, like the Himalayan region, the intrinsic dynamics may play the dominant role in interannual variability; this would imply that even a GCM without interannual variability in lower boundary forcing through SST may produce appreciable skill. We explore this hypothesis based on simulations for the period 1980–2003 with multiple initial conditions with an atmospheric GCM already validated at all-India scale. Multi-scale validation of seasonal forecasts is carried out at regional (Uttarakhand) to station scale over Central Himalaya with multi-source observations. In accordance with our hypothesis and for realizable forecast skill with an atmospheric GCM, the simulations are conducted with climatological monthly SST. At regional (Uttarakhand) scale, the interannual variability in composite observation and ensemble simulation are correlated at 99% significant level, with phase synchronization of about 75%. At station scale, also the skill is found to be non-trivial, especially with respect to gridded observations. Our results thus provide an effective methodology for seasonal forecasting at regional scale over certain geographical locations.
Abstract: Most of the studies of the observed variability of the Indian summer monsoon rainfall (ISMR), its prediction and of its impact have involved analysis of an index for ISMR derived by Parthasarathy et al. (1995) or the all-India rainfall during the summer monsoon, available from the India Meteorological Department (IMD) website. Both these indices are based on the average rainfall over the meteorological subdivisions of India. Rajeevan et al. (2006) first derived a gridded rainfall data set for the Indian region which was at a resolution of 1° and subsequently, Pai et al. (2014) have derived a finer resolution (0.25°) rainfall data set for the same region. At present, these data sets are widely used by modelers to generate the ‘observed’ ISMR for assessment of the skill of their models. However, in different studies, different regions are used for averaging the grid data to obtain the ‘observed’ ISMR. For proper assessment and comparison of the skill of the simulations/predictions by different models/versions, it is important that the same region be used for averaging the rainfall to obtain the observed ISMR in each case. Here, we suggest what we consider as the appropriate regions for averaging the rainfall in terms of the 1° and 0.25° to derive/represent ISMR, on the basis of the present understanding of the monsoonal regions and the Indian summer monsoon. We show that the interannual variation of the ISMR thus derived (by averaging rainfall over the regions identified in this study) from gridded data sets is largely consistent with the indices derived as the area weighted sub-divisional rainfall data used in the indices used earlier.
Citation: Sulochana Gadgil, K Rajendran and D S Pai (2019): A new rain-based index for the Indian summer monsoon rainfall, MAUSAM, 70 (3), 485-500
by Jagat Dwipendra Ray, M. Sithartha Muthu Vijayan, Walyeldeen Godah, Ashok Kumar
Position time series from permanent Global Navigation Satellite System (GNSS)stations are commonly used for estimating secular velocities of discrete points on the Earth’s surface. An understanding of background noise in the GNSS position time series is essential to obtain realistic estimates of velocity uncertainties. The current study focuses on the investigation of background noise in position time series obtained from thirteen permanent GNSS stations located in Nepal Himalaya using the spectral analysis method. The power spectrum of the GNSS position time series has been estimated using the Lomb–Scargle method. The iterative nonlinear Levenberg–Marquardt (LM) algorithm has been applied to estimate the spectral index of the power spectrum. The power spectrum can be described by white noise in the high frequency zone and power law noise in the lower frequency zone. The mean and the standard deviation of the estimated spectral indices are 1.46±0.14; 1.39±0.16 and1.53±0.07 for north, east and vertical components, respectively. On average, the power law noise extends up to a period of ca. 21 days. For a shorter period, i.e. less than ca. 21days, the spectra are white. The spectral index corresponding to random walk noise (ca. –2) is obtained for a site located above the base of a seismogenic zone which can be due to the combined effect of tectonic and nontectonic factors rather than a spurious monumental motion. Overall, the usefulness of investigating the background noise in the GNSS position time series is discussed.
Ray, J. D., M. S. M. Vijayan, W. Godah, and A. Kumar (2019), Investigation of background noise in the GNSS position time series using spectral analysis – A case study of Nepal Himalaya, Geod. Cartogr., 68(No 2), 375–388, doi:10.24425/gac.2019.128468
by P Ajilesh, V Rakesh, Sanjeeb K Sahoo and S Himesh
In this study, 32 rainfall events spanning from 2012 to 2014 over the urban Indian city, Bangalore were simulated using the Weather Research and Forecast (WRF) model. Model simulations were carried out with a four‐nested domain initialized with Global Forecast System (GFS) data and the forecast was generated on an hourly basis. The forecasted rainfall at hobli‐level (Bangalore has 34 hobli divisions with an area of each hobli of the order of ~10 km2) was evaluated in terms of their intensity and pattern of spatial distribution by comparing with corresponding rain‐gauge observations. Also, the rainfall forecast skill of the model was evaluated statistically by computing Root Mean Square Error (RMSE), Bias, and Mean Absolute Error (MAE). Thermodynamic variables like Equivalent Potential Temperature, Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), K Index (KI), Lifted Index (LI), and Total Totals Index (TTI) were also derived from simulated model parameters for all the events and verified against corresponding observations. Results showed that the WRF model could simulate the rainfall events and associated thermodynamic features qualitatively; however, there were few hoblis where the relative errors in the forecast were more than 100%. The forecast errors were relatively lower for cases during the south‐west monsoon season compared to other seasons. It was found that the model underestimated thermodynamic indices like CAPE, dew point depression and the simulated LI were positive; these were indicative of model's limitation in simulating intense convection and a possible reason for underpredicted rainfall simulations.
by SrideviJade, T.S. Shrungeshwara and Boddapati Anil
Precipitable Water Vapor (PWV) is estimated using MODIS (Moderate Resolution Imaging Spectro-radio-meter) Terra level 3 data with daily resolution i. e MOD08_D3 at 64 cGPS (continuous Global Positioning System) stations spatially spread over Indian subcontinent between geodetic latitude 5° to 35° N and geodetic longitude of 70° to 96° E. MODIS-PWV is compared with GPS-PWV estimated at these cGPS stations to check the validity of water vapor retrieved from MODIS data in Indian subcontinent. Correlation coefficient (R2) between daily values of MODIS and GPS water vapor is above 0.9 with RMSE (root mean square error) of 2–5 mm for 22 cGPS in peninsular India, above 0.9 with RMSE of 3–6 mm for 5 cGPS in northeast India and above 0.8 with RMSE of 1–9 mm for 26 cGPS in Himalayas. PWV time series at all the cGPS stations indicated distinct seasonal cycle for both MODIS and GPS PWV with high RMSE (~6 mm) in wet months and low RMSE (~3 mm) during dry months. Taking advantage of broad spatial spread of stations and long span of data, model for spatial variability of GPS-PWV for Indian subcontinent is proposed. Inter-annual and seasonal variability of GPS-PWV is discussed in detail for peninsular India, northeast India and Himalayas.
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