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Prakash S.,NCMRWF | Mitra A.K.,NCMRWF | Momin I.M.,NCMRWF | Gairola R.M.,Atmospheric and Oceanic science Group | And 2 more authors.
Mausam | Year: 2015

Reliable information of rainfall over the Indian land and adjoining oceanic regions is crucial for various hydro-meteorological purposes. Multisatellite rainfall products provide global or quasi-global rainfall maps at regular interval and benefits from the relative advantages of infrared and microwave sensors onboard a constellation of Earth-observation satellites. The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) is one of the most widely used quasi-global high resolution rainfall products for a variety of applications. The existing version 6 (V6) of TMPA products underwent substantial changes with additional inputs and consequently version 7 (V7) data sets were formally released in late 2012. The extensive error characterization of this new version of TMPA data sets is a prerequisite for its widest applicability. This paper highlights the results of recent evaluations of TMPA-3B42 and 3B43 products over the Indian land and oceanic regions against ground-truth observations. Comparison of both the versions of TMPA data sets over the Indian Ocean using gauge observations from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys at monthly scale shows that even though the error associated with higher rainfall is reduced in the V7, the new version shows overall larger bias and root-mean-square error as compared to its predecessor V6. TMPA V7 product is further evaluated at daily scale for an eight-year period (2004-2011) against RAMA buoy observations which shows that TMPA V7 overestimates rainfall compared to observations. However, TMPA V7 underestimates light and heavy rainfall events and the error characteristics show a considerable seasonal variation. The comparison of both the versions of TMPA data sets against gridded gauge-based rainfall data sets over India for the southwest monsoon period of 1998-2010 shows a marginal improvement in V7 over V6, especially in terms of reduced bias. Moreover, TMPA V7 shows better skill than the other contemporary multisatellite rainfall products over India and can be used with higher confidence for monsoon-related studies. Finally, the potential of combined use of multisatellite and local gauge data sets for better rainfall estimation is discussed and the scope for optimal rainfall estimation over the Indian monsoon region in future perspective is recommended. © 2015, India Meteorological Department. All rights reserved.


News Article | August 22, 2016
Site: www.spie.org

Effect of new radiance observations on numerical weather prediction models The impact of humidity observations, from the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry instrument, on an existing unified model assimilation system is investigated. The assimilation of any new observational dataset into a numerical weather prediction (NWP) system can affect the quality of the existing datasets, with respect to the model background (the short-term forecast). This, in turn, influences the use of the existing observations within the NWP system. Indeed, it is the standard practice of operational NWP centers to assess the quality of observations with respect to NWP model fields. Furthermore, the importance of using NWP fields to assess the data quality from microwave sensing instruments has already been shown.1–3 The influence of a new dataset—from the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) instrument—on existing NWP models therefore needs to be assessed. The SAPHIR instrument is a six-channel microwave humidity profiler on the Megha-Tropiques (MT) satellite. The six channels are close to the absorption band of water vapor (at about 183GHz) and thus provide a relatively narrow weighting function, from the surface to an altitude of 10km, for retrieving water vapor profiles in the cloud-free troposphere. The new radiance/brightness temperatures (TBs) from SAPHIR have recently been added to the UK Met Office's Unified Model (UM) assimilation system, which is being used in operations at India's National Centre for Medium Range Weather Forecasting (NCMRWF). In this work,4 we have performed a detailed investigation of the impact of incorporating SAPHIR radiance data into the UK Met Office's UM (i.e., which is used for NWPs). This UM assimilation system is based on incremental 4D-Var, which is a system used to minimize a cost function (penalty function) in the UM. The 4D-Var system describes the departure of the analysis from the background and the observation, which is distributed within a given time window. The forecast model has a horizontal resolution of 25km (at mid-latitudes) and 70 vertical levels between the surface and 80km altitude. For our data assimilation experiments with SAPHIR, however, we used the UM model with a reduced horizontal resolution of 40km. We use so-called innovations—the differences between the observations and the simulations that are based on forecast fields—to diagnose the biases in our observations. Histograms of these innovations, from before and after the bias corrections, are indicative of how well the bias correction works. The departures of the SAPHIR (channel 1) TBs from the simulations, with and without applying the bias correction, are shown in Figure 1. We find that our bias correction (the dotted curve) shifts the mean of the innovation toward zero, which clearly demonstrates the effectiveness of the bias correction. Radiances from four hyperspectral instruments—the IR Atmospheric Sounding Interferometer (IASI) on the MetOP-A and MetOp-B satellites, the Advanced IR Sounder (AIRS) on the Aqua satellite, and the Cross-track IR Sounder (CrIS) on the Suomi-National Polar-orbiting Partnership satellite—are routinely assimilated in the UM 4D-Var system. The percentage differences in the mean of the standard deviation and the assimilated observations for our experiment (i.e., including SAPHIR radiances) and our control (without SAPHIR radiances), with respect to the IASI TBs, are shown in Figure 2. We find a negative value for the standard deviation difference, but a positive value for the observation count. This indicates the positive impact of assimilating SAPHIR TBs on the IASI data. In addition, the assimilation of the SAPHIR TBs means that the standard deviation of the TBs from most of the assimilated IASI channels is reduced to 2–2.5%, and that the number of assimilated TBs increases to about 1%. We have also observed similar effects for the other two hyperspectral datasets (from AIRS and CrIS). Although SAPHIR is a microwave instrument, the impact of its assimilation is clear in the use of hyperspectral radiances in the 4D-Var data assimilation system, as well as on other microwave and IR radiance datasets. We have also analyzed the combined effect of various microwave humidity sounders and imagers on hyperspectral radiances. We used humidity information from the Special Sensor Microwave Imager/Sounder (SSMI/S) instruments on Defense Meteorological Satellite Program satellites and from the Advanced Microwave Scanning Radiometer (AMSR-2) on the Global Change Observation Mission-Water (GCOM-W1) satellite, combined with the high-resolution (vertical) SAPHIR data to improve the performance of hyperspectral instruments in the data assimilation system. Our results demonstrated the complementarity of SAPHIR to the other microwave imagers and hyperspectral instruments. For these analyses, we conducted global assimilation experiments relative to a full observing system. We also included a number of additional observations, i.e., from AMSR-2 imager channels, SSMI/S imager channels, SAPHIR sounding channels, and a combination of all three. The effect of these experiments on background fits to hyperspectral IR sounder observations is illustrated in Figure 3. We find that the assimilation of AMSR-2 or SSMI/S imager information leads to improved fits to the IR window channels (around 800–1200cm−1), which are sensitive to column humidity, whereas small degradations are observed for the water vapor sounding channels above 1400cm−1. Furthermore, the assimilation of SAPHIR data tends to generate better fits (i.e., with reduced standard deviations) for both these spectral regions. Assimilating the combination of all the observation types leads to the largest improvements, which suggests that the 183GHz information from SAPHIR can help to constrain vertical humidity increments in the 4D-Var system. In summary, we have studied the effect of assimilating new SAPHIR microwave humidity observations into the existing UM 4D-Var assimilation system (which is used for numerical weather predictions). Our results indicate that we can successfully assimilate the data from all six SAPHIR channels and that the assimilation positively affects the assimilation of observations from other satellite instruments (in both the IR and microwave regions). The SAPHIR radiance assimilation leads to a reduction in standard deviations of hyperspectral radiances and to an increase in the number of assimilated observations. Furthermore, the assimilation of SAPHIR data, combined with information from microwave images (e.g., SSMI/S and AMSR-2) provides a greater impact than from the individual assimilations. In addition to improving the short-range fits to independent humidity-sensitive observations, the assimilation of SAPHIR data significantly improves the short-range fits to the tropospheric temperature sounding and window channels of advanced IR instruments (i.e., IASI, AIRS, and CrIS) and the influence of the microwave imager radiances. In our future work, we propose to further investigate the benefits of the SAPHIR data. To that end, we will conduct a series of single-observation experiments that will help elucidate the complementarity of microwave imager and SAPHIR datasets. Indira Rani acknowledges the National Monsoon Mission of the Ministry of Earth Sciences, India, for funding her visit to the Met Office, UK, to conduct this work. The authors are also grateful to the head of the National Centre for Medium Range Weather Forecasting for his consistent encouragement.


Prasad V.S.,NCMRWF | Johny C.J.,NCMRWF | Sodhi J.S.,Andhra University | Rajagopal E.N.,NCMRWF
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

Performance of an EnVar hybrid data assimilation system based on 3D Var NGFS (NCMRWF Global Forecast System) of T574 configuration and Ensemble Kalman Filter is investigated. The experiment is conducted during the Indian monsoon season (June-September) 2015 and compared against operational GSI 3D Var system. Two way coupled dual resolution hybrid system with 80 member ensemble of T254L64 configuration are used and forecasts are done for 10days. In hybrid experiment 75% weight is given to ensemble covariance and 25% for static covariance. The forecast skill of experiments over different spatial domains is compared against observations and respective analysis. The hybrid experiment produced significant improvement in forecasts compared to 3D Var in all fields except lower level temperature over tropical regions. Improvement is also seen in the prediction of extreme rainfall events. The prediction of monsoon onset and track of cyclone Ashobaa with hybrid and 3D var system is discussed. © 2016 SPIE.


Johny C.J.,NCMRWF | Prasad V.S.,NCMRWF | Singh S.K.,NCMRWF | Basu S.,Ministry of Earth science
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

Convective downdraft motions and related outflow wind considered as an eventual source of potential damage which can be more severe in the aviation sector. A great variety of atmospheric environments can produce these downdraft motions. These events are not easily detectable using conventional weather radar or wind shear alert systems, while Doppler radars are useful for identifying these Downbursts. In order to identify the situations that can cause these downdraft events different diagnostic tools are designed. Recently launched Indian satellite INSAT-3D, with atmospheric sounder and imager on board, is capable of identifying regions of downburst occurrence and can help in monitoring them in real time. Some Downburst events reported over different parts of India, during January-April period is investigated using Microburst Wind Speed Potential Index (MWPI) and thermodynamic characteristics derived from the NCMRWF GFS (NGFS) model. An attempt is made to make a short range prediction of these events using MWPI computed from NGFS model forecasts. The results are validated with in-situ observations and also by employing INSAT-3D data and it is shown that the method has a reasonable success. All the investigated downdraft events are associated with the hybrid Microburst environment. © 2016 SPIE.


Rajan D.,NCMRWF | Iyengar G.R.,NCMRWF | Mitra A.K.,NCMRWF
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

The climate of India is dominated by monsoon systems. The remotely sensed estimates obtained from the Tropical Rainfall Measuring Mission (TRMM) are used to examine the most of the Indian monsoon systems. This study deals with the diurnal and spatial variation of precipitation over the Indian region. The precipitation data from TRMM Multi-satellite Precipitation Analysis (TMPA), blended from a variety of sources (including rain gauges over land) and having both daily and 3- hourly output are being used for evaluation of the Numerical Weather Prediction models Basu (2007) of National Centre for Medium Range Weather Forecasting. The precipitation obtained from TRMM 3B42 for this study period has a spatial resolution of 0.25° X 0.25° latitude-longitude. The 3-hourly averaged values are centered at the middle of each 3 hr period. South Asian regions are dominated by seasonal climatic fluctuations and the major rainy season is the southwest monsoon season. In addition to the seasonal fluctuations, Indian summer monsoon is modulated by diurnal fluctuations; nature of diurnal variation of rainfall varies from place to place and depends upon the locations, topography of the region. Diurnal variation of rain-rate, frequency of rain, conditional rain rate, and maximum and minimum rain occurrence is studied. Over Indian tropical region, maximum rainfall over land and Bay of Bengal regions is observed during the late-afternoon and early-morning period, respectively. Drizzle or less rainfall occur frequently in the morning over most land areas, whereas convective activity occurs during the afternoon. The model predicted diurnal cycle of precipitation peaks too early (by ∼3h) and the amplitude is too strong over Indian land region and tropical ocean region. © 2016 SPIE.


Johny C.J.,NCMRWF | Prasad V.S.,NCMRWF
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

The accurate estimation of water vapour with high spatial and temporal resolution is needed for operational weather forecasts and weather and climate research. Moisture representation in numerical weather prediction models is inadequate for forecasting meso-scale precipitation events and accurate information of middle and upper tropospheric moisture determines strength, effectiveness and longevity deep convective processes. Global Navigation Satellite System (GNSS) provides a way for measuring atmospheric humidity continuously at a low operational cost by co-locating GPS receiver with meteorological sensor. Impact of assimilation of GPS-IPW observations from NOAA-NWS and EUMETNET network on NCMRWF GFS forecast is investigated during May-June period in 2014. Impact of assimilation of GPS-IPW observations are not only confined to the regions of dense GPS-IPW network, but can be seen in other regions also. Ingestion of IPW observations impacted prediction of rainfall over the Indian monsoon region even though very few IPW stations located in the region. Impact of assimilation is not uniform on temperature, wind and humidity and different over different region. GPS-IPW observations can impact forecast of individual rainfall events at large and major impact on rainfall forecast is seen in the regions of large integrated precipitable water in the model. In India MoES has already setup many GPS-IPW stations and also some more are in the pipeline. The quality of these present observations from MoES and plans for the future GPS-IPW stations are discussed. © 2016 SPIE.


Rajan D.,NCMRWF | Iyengar G.R.,NCMRWF
International Journal of Earth Sciences and Engineering | Year: 2013

Monsoon onset over Kerala has been considered as the beginning of the Indian principal rainy season, or simply the monsoon season. A variety of monsoon indices are examined to study the different phases of the monsoon season. The usefulness of these dynamical monsoon indices are explored extensively at the National Centre for Medium Range Weather Forecasting (NCMRWF). Three popular monsoon indices have been presented here to study the onset, strength and withdrawal phases of monsoon season during 2012. In general, the indices are able to represent the onset, variability in strength of monsoon and the withdrawal in a reasonable way. As per these indices, the actual date of monsoon onset over the main land is during 27-31 May. These indices computed from the NCMRWF global atmospheric models can be used to forecast the changes in phases of the monsoon system within the season. © 2013 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.


Dube A.,NCMRWF | Ashrit R.,NCMRWF | Ashish A.,NCMRWF | Iyengar G.,NCMRWF | Rajagopal E.N.,NCMRWF
Mausam | Year: 2015

The North Indian Ocean is one of the world’s worst affected areas by tropical cyclones. It is because of its vast coastline and high population density in the coastal areas that the damage to life and property caused by a landfalling tropical cyclone is huge. Therefore, timely prediction of the cyclone track, landfall location and time is of critical importance for this region. In the present study a comparison is made between the relative skills of a deterministic model NGFS (NCMRWF Global Forecast System) and an ensemble prediction system (EPS) NGEFS (NCMWRF Global Ensemble Forecast System) in predicting the tropical cyclone track. Four cases of recent cyclones, i.e., Phailin (9-12 October 2013), Helen (19-23 November, 2013), Lehar (23-28 November, 2013) and Madi (6-12 December, 2013) are considered for this comparison. Except of Helen which was a Severe Cyclonic Storm (SCS), all the above cyclones were in the category of Very Severe Cyclonic Storms (VSCS). Further an attempt is made to correct the systematic biases in NGEFS model by using the method of moment adjustment. A comparison of the performance of the models is made on the basis of along track, cross track and direct position errors obtained from the forecast tracks from the three models and the IMD best track data. It is seen that for a cyclone like Phailin which did not show any sudden changes in the track the mean of NGEFS shows a lower track error as compared to NGFS and the bias corrected output from NGEFS shows a further improvement in the TC track forecast. However, in the case of Madi which showed a sudden change in the direction NGEFS showed a better forecast before the direction change as compared to both NGFS and the bias corrected NGEFS. But after the change in the direction NGEFS with bias correction is seen to be performing better than NGEFS and NGFS. On an average for the four cyclone cases of 2013 it is seen that the bias correction leads to an improvement of about 17% in the initial position error as compared to raw ensemble track forecast and about 38% when compared with the deterministic model. In the day 5 forecasts the improvement in the bias corrected ensemble forecast as compared to NGEFS and NGFS are 24% and 17% respectively. © 2015, India Meteorological Department. All rights reserved.


Mohandas S.,NCMRWF | Singh H.,NCMRWF
Mausam | Year: 2015

The current study demonstrates the utilisation of a tool for the comprehensive evaluation of model forecasts using both traditional and spatial diagnostic techniques. The fundamental idea is to provide additional and meaningful insight into the model weaknesses and strengths in terms of underlying physical processes especially for very high resolution models and observations. The traditional scores also suffer from the so called “double penalty” issue and hence alone cannot provide a measure of spatial and temporal match between the forecast and observed rainfall patterns. Method for Object-based Diagnostics Evaluation is a spatial verification technique in the category of displacement methods while wavelet analysis comes into filtering type of spatial verification. Former is a features based verification technique while the latter is based on scale-separation principle. The case of Very Severe Tropical Cyclone ‘Phailin’ is taken up for the study and the rainfall forecasts from Global Forecast System and Unified Model run at National Centre for Medium Range Weather Forecasting are verified against gridded satellite-cum-raingauge-merged rainfall analysis. The traditional verification scores were computed using categorical and continuous measures and the spatial verification scores were computed against various thresholds. The results are presented to summarise the overall performance of both the global models with respect to the rainfall prediction. © 2015, India Meteorological Department. All rights reserved.


Aditi,NCMRWF | George J.P.,NCMRWF | Das Gupta M.,NCMRWF | Rajagopal E.N.,NCMRWF | Basu S.,NCMRWF
Mausam | Year: 2015

Forecast of fog and visibility over most parts of Indo-Gangetic plains are becoming increasingly important in the winter season because of the high frequency of occurrence of dense fog and reduced visibility which has significant socio-economic impacts. The life cycle of fog is mainly controlled by different meteorological factors and the microphysical/chemical properties of the particulate matter in the atmosphere. The present day numerical weather prediction (NWP) models of high spatial resolution are able to forecast situations that are favorable for the occurrence of fog events with reasonable accuracy for few days in advance. NCMRWF has started producing visibility/fog forecasts using the Unified Model (NCUM), which has a diagnostic fog scheme. The visibility is computed in the model based on the extinction of light at visible ranges due to fog particles. The visibility/fog forecasts during the months of December, 2013 and January, 2014 obtained from NCUM over the Indo-Gangetic plains are verified using the surface as well as satellite observations in this study. Surface visibility observations from meteorological airport reports (METAR) and satellite based fog product from Moderate Resolution Imaging Spectroradiometer (MODIS) are used for the verification. NCUM short-range forecast shows good skill in indicating the occurrence of fog/no-fog events, based on two visibility categories defined in this study to represent the heavy and light fog events, over different locations over the Indo-Gangetic plains. © 2015, India Meteorological Department. All rights reserved.

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