Time filter

Source Type

Greater Noida, India

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. Source

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. Source

News Article
Site: http://www.spie.org/x2420.xml

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.

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. Source

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. Source

Discover hidden collaborations