National Centre For Medium Range Weather Forecasting Research Articles

The impact of hyperspectral radiances in the NCUM 4D-Var data assimilation system

ABSTRACT This paper describes the combined impact of clear sky hyperspectral radiances from four space-based instruments in the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) 4D-Var assimilation and forecast system through Observing System Experiment (OSE) during May 2018. Radiances from four hyperspectral instruments, Atmospheric Infrared Sounder (AIRS) onboard EOS-AQUA satellite, Infrared Atmospheric Sounding Interferometer (IASI) onboard both MetOp-A and MetOp-B satellites and Cross-track Infrared Sounder (CrIS) onboard NPP satellite along with other conventional and satellite observations are assimilated in the first simulation (EXP), while the radiances from the four hyperspectral instruments were denied in the latter simulation (CNTL). The simulations run four assimilation cycles per day centred at 0000, 0600, 1200, and 1800 UTCs. 5-day forecasts based on the 0000 UTC initial conditions are generated from both EXP and CNTL. Results show that both O-B (Observation-Background) and O-A (Observation – Analysis) of other satellite radiances assimilated in EXP improved (in this case biases reduced) due to the assimilation of hyperspectral radiances. The impact of hyperspectral data assimilation in various forecast fields is estimated by computing Forecast Impact (FI) parameter with respect to an independent dataset, ERA5. Hyperspectral radiance assimilation improved both mass and wind forecasts in the short-range, and the FI degraded with higher lead time. There is a slight improvement in the precipitation over the Indian landmass due to the assimilation of hyperspectral radiances. Verification of model forecasts with radiosonde observations over the Indian region shows marginal improvement in the humidity and temperature forecasts due to the assimilation of hyperspectral radiances. Assimilation of hyperspectral radiances slightly improved the characteristics of two cyclones, Sagar and Mekunu, formed over the Arabian Sea during the study period. Both the experiments predicted early onset of monsoon 2018 than the IMD date of monsoon onset.

Impact of Aeolus horizontal line of sight wind observations in a global NWP system

This study evaluates the impact of Aeolus horizontal line-of-sight (HLOS) wind observations in the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) assimilation and forecast system from 20 May to 22 June 2020. The quality of both Rayleigh clear and Mie cloudy Aeolus HLOS winds are validated during the study period and found to be useful for assimilation. Observing System Experiment (OSE) and adjoint-based Forecast Sensitivity to Observation Impact (FSOI) methods are used to assess the impact of the Aeolus HLOS wind observations in the global NCUM system. The Indian Summer Monsoon onset and the life cycle of a severe cyclonic storm Nisarga formed over the eastern Arabian Sea are investigated. The FSOI study clearly shows that HLOS wind observations significantly impact the model forecast, especially over the Tropics and Southern Hemisphere extra-tropics. The impact of HLOS winds in the upper troposphere and above is more marked. Forecasts with the assimilation of the HLOS winds enhanced the circulation during the onset of the Indian summer monsoon and weakened the Monsoon Low Level Jet (MLLJ) when the Nisarga cyclone formed over the Arabian Sea. The high-resolution wind observations from Aeolus reduced the position error of the tropical cyclone Nisarga in the NCUM analysis.

On the Generation of Daily Gridded Ocean Surface Vector Wind Products From Scatsat-1

An optimal interpolation (OI)-based technique is utilized to generate global daily gridded wind products from Scatsat-1 data. The first guess (i.e., background) for the OI is used from the analyses of National Centre for Medium Range Weather Forecasting (NCMRWF) global operational model. The generated gridded products are subsequently validated using observations from global moored buoys and surface winds analyses from European Centre for Medium-Range Weather Forecasts (ECMWF), and that shows a root mean square error of ~1.3 m/s in wind speed and ~19° in wind direction. Along with the gridded winds, several wind-dependent products (e.g., wind stress, wind divergence, wind stress curl, sensible and latent heat flux, etc.) are also generated. The gridded products from Scatsat-1 are available freely from Meteorological and Oceanographic Satellite Data Archival Center ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://www.mosdac.gov.in</uri> ) since October 03, 2016.

Assessing the skill of NCMRWF global ensemble prediction system in predicting Indian summer monsoon during 2018

The quality of probabilistic precipitation and zonal wind forecasts from National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Prediction System (NEPS-G) is investigated for Indian summer monsoon period between June–September 2018. Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG GPM) are used for verification of precipitation forecasts. The predictive skill of different categories of rainfall is examined with respect to daily climatology based on Tropical Rainfall Measuring Mission (TRMM) observations and reanalysis data from the Indian Monsoon Data Assimilation and Analysis (IMDAA). ERA Interim and IMDAA reanalysis daily climatologies are used to compute skill for the zonal wind forecasts at 850 hPa (u850). The model has a systematic tendency to over-predict the low level westerlies associated with the monsoon circulation. RMSE over Gangetic plains near Himalayan foothills is more in day-3 as compared to subsequent forecast lead times due to its overestimation of the easterly zonal wind flow. Spread in u850 is comparable to RMSE in day-1 forecast. The ensemble forecasting system is slightly under-dispersive for longer forecast lead times, since the rate of growth of forecast uncertainty is larger than that could be predicted by the ensemble system. Forecasts are sharper for lower thresholds of rainfall and exhibit more reliability and better discrimination of events over shorter lead times. Similar to reliability, the rank distribution depends on forecast lead time as well as ensemble spread. The positive Brier skill score and Continuous Ranked Probability Skill Score values above 0.4 for probabilistic wind as well as precipitation forecasts of light to moderate category, consistently show high predictive skill till day-7, with reference to the long-term climatology. NEPS-G could predict an extreme rainfall event with high probabilities of precipitation exceeding thresholds classified by India Meteorological department, which are in good correspondence with that of rainfall observed by GPM IMERG. A monsoon index based on large-scale features of monsoon circulation could be predicted by the EPS with high probabilistic skill during the peak monsoon.

Predicting the rapid intensification and dynamics of pre-monsoon extremely severe cyclonic storm ‘Fani’ (2019) over the Bay of Bengal in a 12-km global model

The present study investigates into genesis, intensification, dynamical behavior and the prediction of Extremely Severe Cyclonic Storm (ESCS) ‘Fani' over the Bay of Bengal (BoB) in the National Centre for Medium-Range Weather Forecasting (NCMRWF) global numerical weather prediction (NWP) modeling system ‘NCUM.’ The global model is adopted from the UK Met office's Unified Model (UM). The ESCS ‘Fani' formed from 26 April–04 May 2019, over the warm waters of BoB. In the criterion of landfalling tropical cyclones (TCs), which crossed the Orissa coast, it was the most intense cyclonic storm during pre-monsoon season since 1965. The TC exhibited a markedly different characteristic having genesis very near to the equator (near 2.7°N and 88.7°E) and possessing one of the longest tracks (~3030-km) over the BoB region. The NCUM global model was operationally run during the occurrence of the TC. The diagnosis of the TC's genesis and rapid intensification (RI) in the model is carried out using various metrics such as Genesis potential parameter (GPP), winds at 850-hPa, vertically integrated moisture flux, potential vorticity (PV) at isentropic level 315 K, Severe Weather Threat (SWEAT) index and daily averaged latent heat flux, etc. It is found that the early genesis, structure, RI, and movement of the TC were well captured by the model in advance. The Model predicted TC tracks for a total of seven initial conditions (from 27 April to 03 May 2019, at 24-h interval) are found closely co-related with the observed best track of the TC provided by India Meteorological Department (IMD). The lower values of Direct Position Errors (DPEs), along-track errors (ATEs), and cross-track errors (CTEs) indicate the skillful prediction of the TC by the model. The structure, amplitude, and location (SAL) simulated precipitation of the TC over the BoB and along the central-east coast of India are in agreement with the observed systems. Our detailed diagnostic analysis suggests that the TC formation, development, and intensification was mostly controlled by the warmer BoB sea surface temperatures.

Simulation of a mesoscale convective system over Northern India: Sensitivity to convection partitioning in a regional NWP model

Simulations of a mesoscale convective system (MCS), which propagated across Northern India on 2nd May 2018 - leading to many fatalities when the gust front knocked down homes and tore apart building roofs - have been performed using the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model – Regional (4 km horizontal grid spacing), to evaluate the model’s convective treatments. Though the model captures many of the qualitative and quantitative features, it slightly lags behind the observed MCS organisation and movement, produces lesser precipitation, and lacks the spatial separation between two adjacent organised convective systems in the satellite observations – leading to a faintly offset MCS track. Sensitivity simulations are then performed, for this non-equilibrium MCS case, with different partitioning between parametrized and explicit convection to assess the reliance of the convective treatments on the large-scale environment, as well as to test the notion of a breakdown of convective parametrization at the mesoscale model resolution. Fully parametrized (FP) convection produces even lesser rainfall and are dominated by orographic precipitations along the foot hills of Himalayas with no any trace of the MCS. Fully explicit (FE) convection realistically simulates most of the prominent convective cells and enhance precipitation along the MCS track that agree better with the observations, though the ‘two lobes’ of intense precipitation are not resolved; instead it produces a squall line of precipitation. The FE configuration generates the most vigorous convective updraft, along with a vertical shear that is tilted westward. The simulation with partially parametrized and partially explicit convection resembles the fashion in the FP and FE scenarios, with a transition over the duration of the run from parametrized to explicit precipitation. The results are in line with the notion from previous studies; that the majority of successful explicit simulations of mesoscale organisation are those associated with strong large-scale forcing for convection, wherein resolved vertical motions are sufficient to minimise delays in onset.

NWP perspective of the extreme precipitation and flood event in Kerala (India) during August 2018

In depth examination of the extreme precipitation over Kerala during 15–17th August 2018 – triggering major flood episode in the peninsular Indian state - have been made via the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model. A rare alignment of an intense low pressure area (LPA) over head Bay of Bengal (BoB) and a couple of typhoons over South China Sea and West Pacific, driven by anomalous sea surface conditions along the south-east Asian coastal belts, kept the low level jet meander from central Arabian Sea to West North Pacific (WNP) and remain relentless throughout the episode. Positive velocity potentials (VP, at 850 hPa) were seen prevailing for the region and some of these VP fringes unevenly crossed over to Kerala - suggestive of an uneven wind convergence – and associated rainfall necessarily not dictated by orography alone. A single system of negative stream functions (SF) suggested the prevalence of a conveyor belt like flow – termed, the ‘Remotely Aligned Intense Tropical Circulations’ (RAITC) – helping bring ‘an additional supply of moisture’ from WNP to Kerala; the cyclones over the WNP provided large amounts of moisture to the upper air and while some of these cyclones were weakening, a part of this moisture got injected to the south-westward flow and was then propelled further by the BoB LPA. While the NCUM global forecasts could capture the general circulation pattern for the intense precipitation over Kerala in the medium range, the spatial distribution of the heavy spells couldn’t be captured well in day-7 forecast; the strength and movement of the BoB LPA as well as the alignment and intensity modulation of the multiple tropical circulations showed some deviations. The convection permitting regional version of the model, NCUM-R (4 km grid length), exhibited lesser frequency for the light rainfall events, while over-predicting the frequency for the extreme precipitations.

Prediction of the August 2018 heavy rainfall events over Kerala with high‐resolution NWP models

AbstractThe southern Indian state of Kerala experienced exceptionally high rainfall during August 2018, which led to devastating floods in many parts of the state. Prediction and early warning of severe weather events in vulnerable areas is crucial for disaster management agencies in order to protect life and property. In recent years, state‐of‐the‐art numerical weather prediction (NWP) models have been used operationally to predict rainfall over different spatial and temporal scales. In the present paper, predictions based on the National Centre for Medium Range Weather Forecasting (NCMRWF) models (NCUM, NCUM‐R and NEPS) are assessed over Kerala to demonstrate the capabilities of high‐resolution models. It is found that the deterministic NWP model (NCUM and NCUM‐R) forecasts are accurate at shorter lead times (up to Day 3) mainly in terms of timing and, to some extent, intensity. At higher lead times (beyond Day 3), the ensemble‐based probabilistic forecasts are useful and actionable.

Phase inconsistency as a major source of error in NGFS forecast

South Asian monsoon exhibits multiscale spatiotemporal variability. Analyzing the nature and behavior of numerical weather forecast error associated with these space-time heterogeneities will eventually help in improving the models. We investigate the spatiotemporal error characteristics of the National Centre for Medium-Range Weather Forecasting (NCMRWF) Global Forecast System (NGFS) model over South Asian land and ocean separately. Although error grows with lead-time, it saturates within 3–5 days of forecast initiation. The saturated error is only about 15–25% higher than that of day-1, indicating that most of the error accumulates within first 24-h of forecast. Increase in error over oceanic regions is due to an increase in the area with high error at all precipitation ranges with large day-to-day variability. However, over land error growth is primarily confined at locations of high mean precipitation. Decomposition of error arising due to intensity and phase variations reveals that about 90% of it arises from the model’s inability to capture phase of precipitation at various timescales. We show that NGFS cannot capture synoptic scale variations ($$<10$$ day) after day-2. Both the high-frequency (10–20 day) and low-frequency (30–60 day) intraseasonal variations are reasonably predicted up to day-3. At diurnal timescale, NGFS forecasts show a peak in precipitation about 3–6 h prior to that observed, both over land and ocean. Surprisingly, this error does not change with lead-time. Lastly, we show that major error characteristics do not depend on the seasonal mean monsoon rainfall.

Role of Atmospheric Boundary Layer (ABL) Height and Ventilation Coefficient on Urban Air Quality- A study based on Observations and NWP Model

Air pollution is an issue of great concern in any urban region due to its serious health implications. The capital of India, New Delhi continues to be in the list of most polluted cities since 2014. The air quality of any region depends on the ability of dispersion of air pollutants. The height or depth of the atmospheric boundary layer (ABL) is one measure of dispersion of air pollutants. Ventilation coefficient is another crucial parameter in determining the air quality of any region. Both of these parameters are obtained over Delhi from the operational global numerical weather prediction (NWP) model of National Centre for Medium Range Weather forecasting (NCMRWF) known as NCMRWF Unified Model (NCUM). The height of ABL over Delhi, is also obtained from radiosonde observations using the parcel method. A good agreement is found between the observed and predicted values of ABL height. The maximum height of ABL is obtained during summer season and minimum is obtained in winter season. High values of air pollutants are found when the values of ABL height and ventilation coefficient are low.

Medium‐range global ensemble prediction system at 12 km horizontal resolution and its preliminary validation

AbstractForecasts of high‐impact weather systems require sufficiently high resolution of state‐of‐the‐art numerical models in order to resolve the small‐scale features. At the National Centre for Medium Range Weather Forecasting (NCMRWF) in India, a global ensemble prediction system (EPS—called the NEPS) has been implemented operationally at 12 km horizontal grid size. The NEPS configuration is based on the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS). Initial condition perturbations are generated by the ensemble transform Kalman filter (ETKF) method. Model uncertainties are taken care of by stochastic kinetic energy backscatter (SKEB) and random parameters (RP) schemes. Forecast perturbations obtained from 6 hr short forecasts of 22 ensemble members are updated by the ETKF four times a day (0000, 0600, 1200 and 1800 UTC). Perturbations of surface parameters such as sea‐surface temperature, soil moisture content and soil temperature are also included in the new NEPS. The NEPS aims to provide 10 day probabilistic long forecasts using 23 ensemble members (22 perturbed plus one control). The long forecast provided at 0000 UTC is the combination of 11 members from the 0000 UTC cycle and lagged 11 members from the 1200 UTC cycle. The new NEPS shows improvements in terms of forecast agreement among the members in comparison with the previously operational NEPS that was running at 33 km horizontal grid size with 44 perturbed members. The ratio between the root mean square error of the ensemble mean and ensemble spread as a function of lead time has improved in both the Northern and Southern Hemispheres in the new NEPS.

Assimilation of INSAT-3D imager water vapour clear sky brightness temperature in the NCMRWF’s assimilation and forecast system

This paper describes the direct assimilation of water vapour (WV) clear sky brightness temperatures (CSBTs) from the INSAT-3D imager in the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) assimilation and forecast system. INSAT-3D imager WV CSBTs show a systematic bias of 2–3 K compared to the data simulated from the model first guess fields in the pre-assimilation study. The bias in the INSAT-3D imager WV CSBTs is removed using a statistical bias correction prior to assimilation. The impact of INSAT-3D imager WV channel CSBTs is investigated through different approaches: (i) single observation experiments and (ii) global assimilation experiments using the hybrid-four-dimensional variational technique. Single observation experiments of channels of the same frequency from different instruments like the INSAT-3D imager and sounder, and the Meteosat visible and infrared imager (MVIRI) onboard Meteosat-7, show the INSAT-3D imager and MVIRI WV channels have a similar impact on the analysis increment. Global assimilation clearly shows the positive impact of the INSAT-3D imager WV CSBTs on the humidity and upper tropospheric wind fields, whereas the impact on the temperature field, particularly over the tropics, is neutral. Validation of model forecasted parameters with the in situ radio sonde observations also showed the positive impact of assimilation on the humidity and wind fields. INSAT-3D imager WV CSBTs have been assimilated operationally in NCUM since August 2018.

A Bayesian Estimation Technique for Improving the Accuracy of SCATSAT-1 Winds in Rainy Conditions

In this study, we present a mean-of-posteriors Bayesian estimation technique for improving rain contaminated wind speed estimates from the SCATSAT-1 scatterometer, particularly at low and moderate wind speeds. The performance of the algorithm is assessed using the SCATSAT-1 Version 1.1.3 data, collocated National Centre for Medium Range Weather Forecasting (NCMRWF) analysis winds, measurements from the Advanced Scatterometer (ASCAT), and buoy measurements for a 6-month period from April to September 2018. The validation results show a reduction in wind speed bias from 2.6 to $-$ 0.1 m/s and a reduction in root mean square difference (RMSD) from 4.8 to 2.9 m/s relative to NCMRWF. The results from comparisons with ASCAT and buoy data are also qualitatively similar; the RMSD relative to ASCAT reduces from 2.9 to 1.8 m/s after correction, whereas the RMSD relative to buoy reduces from 4.1 to 2.2 m/s. Different methods such as histogram comparisons, across-track, wind speed dependent, and spatial variation of wind speed errors have also been presented to demonstrate the overall improvement achieved by this technique. This algorithm presents two main advantages: 1) the errors in wind speed estimates are significantly lower after correction and 2) this methodology can be applied on both the inner and outer SCATSAT-1 swath.

Impact of Cartosat-1 orography on weather prediction in a high-resolution NCMRWF unified model

The current study reports for the first time an application of orography from the Cartosat-1 satellite digital elevation model (DEM) generated at a source resolution of 30 m in a convection-permitting numerical weather prediction model. The effects of improvements in the representation of orography have been examined in the high-resolution regional National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model predictions for a heavy rainfall event over the city of Chennai. A time-lagged ensemble method is employed to account for the uncertainties associated with the initial conditions, which can better forecast extreme weather events than single forecasts. The simulations reveal that the predictions based on Cartosat-1 DEM capture the local details of the rainfall distribution better than the National Aeronautics and Space Administration shuttle radar topography mission DEM-based predictions, and better represent the orographic and thermal uplifting. The spatio-temporal patterns of the simulated rainfall over Chennai are superior in Cartosat-1 DEM-based simulations mainly due to the enhanced wind convergence and moisture transport. The present study reveals the role of mountains in the enhancement of heavy rainfall events over coastal cities and highlights the potential use of high-resolution orography in the improvement of the operational weather forecasting skill of the NCMRWF Unified Model.

Comparison of different satellite-derived rainfall products with IMD gridded data over Indian meteorological subdivisions during Indian Summer Monsoon (ISM) 2016 at weekly temporal resolution

In this study, effort is made to estimate weekly rainfall over India at 36 meteorological subdivisions during monsoon 2016 (June to September) by using several precipitation products from different satellites which include Indian National Satellite System (INSAT) INSAT-3D derived Hydro Estimate (HE), INSAT Multi-Spectral Rainfall Algorithm (IMSRA) rainfall products, Integrated Multi-satellite Retrievals for GPM (IMERG) derived from JPL, NASA, and National Center for Medium Range Weather Forecasting (NCMRWF) Merged Satellite Gauge (NMSG) of IMD. Different satellite estimates, and rain gauge satellite merged products have been compared with gridded rainfall products prepared by India Meteorological Department (IMD). HE at 4 km pixel resolution and IMSRA at 0.100 × 0.100 resolution are derived rainfall products from Indian satellite INSAT-3D.GPM (IMERG) product is derived at 0.100 × 0.100 resolution from Global Precipitation Mission (GPM) satellite jointly operated by JAXA (Japan) and NASA (USA). NCMRWF Merged Satellite-Gauge (NMSG) rainfall data has been generated at a 0.25° × 0.25° grid resolution by IMD & NCMRWF. The data is analyzed preliminarily for the southwest monsoon season of 2016 at weekly temporal resolution. The overall comparison shows that GPM (IMERG) estimates better rainfall over the coastal and plain region, however, HE estimates better rainfall over the hilly region, in general, and for Kerala region, particularly. NMSG product needs improvement over the hilly region and considerable improvement is required in IMSRA product. For all 36 meteorological subdivisions, NMSG product has 0.56 average Nash-Sutcliffe efficiencies (NSE) for 17 weeks with IMD Gridded rainfall data. However, for satellite-derived rainfall by GPM (IMERG), HE and IMSRA have 0.29, 0.1 and −0.8 average NSE at 17 weeks, respectively. Based on this study, it is concluded that NMSG daily rainfall product is best, however, amongst satellite-derived rainfall, overall GPM gives a better result with a few exceptions and IMSRA need more modifications.

Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period

ABSTRACTRainfall is one of the key drivers of the global hydrological cycle and has large socio-economic impacts. Tropical rainfall accounts for two-thirds of the global rainfall and is primarily associated with the monsoon. Multi-satellite rainfall products provide rainfall with high temporal and spatial resolutions; however, they exhibit regional and seasonal biases. Evaluation of these products against ground-based observations can improve the accuracy of the estimated rainfall. With the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high-resolution multi-satellite precipitation products namely; Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are released. In the present study the spatial and temporal structures of rainfall in near real time and research versions of IMERG-V4 (near real-time (NRT) & Final (FNL)), GSMaP-V6 (NRT & moving vector with Kalman filter (MVK)), INSAT3D (Indian National Satellite System (INSAT) Multispectral Rainfall Algorithm (IMR) & Hydro-Estimator method (HEM)) and Indian Meteorological Department (IMD) – National Centre for Medium Range Weather Forecasting (NCMRWF) Merged product have been evaluated against gridded gauge-based IMD rainfall data on daily, monthly and seasonal scales. All the datasets show noticeable bias in producing rainfall over orographic regions (i.e. Western Ghats and foothills of Himalayas) and North-East India, though there exists significant difference among the satellite measurements. Different skill scores are computed for GSMaP, IMERG and INSAT3D data products to evaluate the performance of these satellite estimates. However in terms of biases IMD-NCMRWF Merged, GSMaP (NRT & MVK) and IMERG (NRT & FNL) underestimates rainfall (about 11%, 17%, 23%, 18% and 3%, respectively) and INSAT3D (IMR & HEM) overestimates (about 49% and 33%, respectively), for the India region as a whole. In a similar way, HEM product shows 15% better performance than IMR product in INSAT3D category. However, both NRT and MVK products of GSMaP show similar variations compared to observe rainfall. Overall IMD–NCMRWF merged and IMERG-FNL data products show better agreement with the gauge-based IMD data compared to GSMaP. The GPM-based products (IMERG and GSMaP) estimate rainfall much better than INSAT3D estimation.

Computation of skill of a mesoscale model in forecasting thunderstorm using radar reflectivity

An attempt is made to determine skill of a mesoscale model through calculation of distance, intensity and time errors with respect to observed maximum reflectivity data (maxdBZ) of Doppler Weather Radar (DWR). A new approach is adopted to calculate distance, intensity and time errors of the model simulated thunderstorm events. Five cases of pre-monsoon thunderstorms over the eastern part of India are simulated by Advanced Research WRF (ARW) model and the model simulated maxdBZ is validated against DWR data. In all the cases the model is run from two different initial conditions. The global model forecast of National Centre for Medium Range Weather Forecasting (NCMRWF), India is directly used as the initial condition for the control run (Exp1) while in the other run (Exp2) initial condition is prepared by assimilating local synoptic data with 6 h model forecast taken as background. The spatial distributions of model simulated and observed maxdBZ show that the newly adopted method is capable of capturing the model skill through computation of distance error. This newly adopted method is used to investigate whether the model simulation with data assimilation is more skillful in determining distance, intensity and time errors. In all the cases, except one, experiments with data assimilations have shown comparatively less distance error than control experiments in the initial hours of the days of the events. Intensity errors of experiments with data assimilations are marginally better than those of control experiment for two cases. Experiments with data assimilation show less time errors than that of control experiment in some cases but it is not always true.

Dynamical characteristics of forecast errors in the NCMRWF unified model (NCUM)

Real-time medium-range weather forecasts (up to 10 days in advance) are prepared at the National Centre for Medium Range Weather Forecasting (NCMRWF), India using the UK Met Office Unified Model (NCUM) system. In this study, the dynamical characteristics of systematic errors of the NCUM model at 17 km horizontal resolution during the Indian summer monsoon (July and August 2017) are examined. It is found that the model has problem in maintaining the monsoon circulation and it weakens the monsoon by weakening the divergent winds over the Indian region. The model has a large spin up in global mean precipitation and evaporation from day-1 to day-3. In addition to the spin up problem, the model loses mass as the forecast length increases from day-1 to day-10. However, the mean surface pressure over India increases in forecasts from day-3 up to day-10. An anticyclonic circulation error over north Arabian Sea and adjoining region reduces moisture transport from the sea to the Indian land areas. The model has systematic tendency to forecast less rain as the forecast length increases. Differences between the ERA-Interim and the NCUM analysis in circulation and moisture parameters indicate uncertainties in the initial conditions and imbalance in moisture and divergence. Relationship between circulation errors and rainfall errors over a river basin (Mahanadi basin in Odisha) was studied. There is no linear relation between divergent or rotational components of wind errors and rainfall errors in forecasts over this basin. Even with a very small error in wind, the rainfall error could be as large as 10 cm/day or more in the basin.

Megha‐tropiques SAPHIR radiances in a hybrid 4D‐Var data assimilation system: Study of forecast impact

Assimilation of microwave humidity channels from various satellites plays a key role in numerical weather prediction (NWP), especially over the Tropics. This article examines various characteristics and the impact of assimilation of Megha‐Tropiques SAPHIR (Sondeur Atmosphérique du Profil ďHumidité Intertropicale par Radiométrie) microwave radiances on model forecasts along with that of other similar microwave humidity sensors. A hybrid 4D‐Var scheme is used for assimilation of these observations, and forecasts are carried out using an advanced global atmospheric model, National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM). The impact of SAPHIR radiances along with similar kinds of sensors have been studied using adjoint‐based Forecast Sensitivity to Observations (FSO) and Observing System Experiments (OSEs). The lowest altitude peaking channel of SAPHIR (183 ± 11 GHz) has the highest beneficial impact on the forecast compared to other channels of the same instrument and other similar instruments on board other satellites. FSO experiments show SAPHIR and Microwave Humidity Sounder (MHS) observations complement each other by having the higher beneficial impact in the lower and upper troposphere, respectively. The other microwave humidity sounders have less or negligible impact in comparison to SAPHIR/MHS, both globally and over the Indian region. An OSE shows that SAPHIR radiances assimilation influences the humidity and wind forecast of the tropical troposphere, while its impact on temperature is negligible.

Quality and Impact of GPSRO Observations from Megha-Tropique Satellite on NGFS Model

Megha-Tropique satellite mission launched in 2011 was aimed at providing more observations in the tropical region. In the initial phase of the mission, it was found that the quality of global positioning system radio occultation (GPSRO) observations was not satisfactory. The Indian Space Research Organisation (ISRO) took remedial measures in this regard by modifying the data processing algorithm and releasing the new version of data. In 2012, an observing system simulation experiment (OSSE) was done at National Centre for Medium Range Weather Forecasting (NCMRWF) using simulated data at Megha- Tropiques ROSA observation location with Global Forecast System (GFS) based model. As an extension of the previous study, the quality of new version of GPSRO bending angle observations and impact of assimilation of these observations in NCMRWF GFS (NGFS) model were studied. It was found that with the use of a new data processing algorithm, quality of bending angle observations improved and comparable with other GPSRO missions in the pressure range between 500 hpa and 200 hpa. Impact study shows that the new observations improved forecasts in the middle and upper levels in the tropics.