National Centre For Medium Range Weather Forecasting Research Articles

Unraveling Subseasonal Drought Dynamics in India: Insights from NCMRWF Extended Range Prediction System

Abstract Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.

Grand ensemble forecasts verification based on two high resolution (∼12 km) global ensemble prediction systems

The primary objective of this study is to quantitatively assess the skill of grand-ensemble forecasts generated from two high-resolution global ensemble prediction systems (EPSs). We have used the National Centre for Medium Range Weather Forecasting (NCMRWF), India global EPS, denoted as NEPS, and India Meteorological Department (IMD) EPS, known as GEFS. Both of these operational EPSs operate at ∼12 km horizontal grid size and produce ensemble forecast outputs. In this study, these two high-resolution EPSs based grand-ensemble products have been evaluated.Statistical analysis is conducted using seven-day forecasts from NEPS's 22 perturbed members and GEFS's 20 perturbed members. The performance of the global grand ensemble, consisting of 42 members, is evaluated for summer monsoon season, 1st July to 30th September 2019 in comparison to the individual EPS forecasts. The verification metrics are prepared using variables such as Zonal Wind and Temperature at 850 hPa, Geopotential at 500 hPa and Precipitation. These metrics are applied across both the Northern Hemisphere (20°N-90°N) and over the domain located in the South Asia encompassing India and its neighbouring areas (0–40 oN & 50–100°E). The verification metrics employed include ensemble spread, root-mean-square error (RMSE) of ensemble mean, Brier score, relative operating characteristic, area skill score, reliability, rank histogram, ranked probability score, continuous ranked probability score, and relative economic value.We noted that the grand ensemble forecasts in this study carries a higher skill compared to the two constituent models for lead time >3 days and for higher forecast probability for the longer lead times. Lower RMSE in the grand ensemble for longer lead-time forecasts (day 4–7) indicates a better representation when compared to forecasts from individual EPSs. However, it is worth noting that shorter lead-time forecasts (day 1–3) in the grand ensemble could potentially result in over-dispersion. Brier scores further indicate an enhancement in predictability of nearly one day in grand ensemble compared to the individual EPSs for T850, U850 and Z500. RPS of rainfall in a smaller domain centring on India indicates a better score for grand ensemble compared to both the EPS. Grand ensembles demonstrate its ability to provide better reliability and discrimination quality. The economic value of the forecasts based on grand ensemble, is higher with a wider range of cost/loss ratio compared to those in the individual EPSs.

Prospects and status of forecasting monthly mean subregional rainfall during the Indian summer monsoon using the coupled Unified Model

AbstractWhile there is huge demand for regional forecasts, information needed for selection of the most appropriate temporal and spatial scales is not available. The objective of this study is to demonstrate the basis of forecasting monthly mean rainfall over homogeneous regions by analyzing the forecasting skill and source of predictability. Reforecasts generated at the National Center for Medium Range Weather Forecasting (NCMRWF) for the period 1993–2015 using the coupled Unified Model are used in this study. Analysis of the forecasting skill over increasingly large lead times, averaging periods and spatial scales, is carried out to compare the skill at different time‐scales and to highlight the effect of spatial averaging over regions of coherent rainfall characteristics. Analysis of probabilistic forecasts is carried out to further demonstrate the usefulness of monthly mean forecasts. The influence of forcings on rainfall is studied both in model and in observations to understand the model's skill in representing interannual variability of monthly mean rainfall. Multiple regression analyses carried out for rainfall using climate indices as independent variables shows that the extent of forcings can largely explain the high variability of rainfall during the onset and withdrawal phase compared to the peak phase of monsoons. ENSO‐related subsidence is found to influence mainly the southern peninsular region, while tropical sea surface temperatures (SSTs) in the Indian Ocean are found to influence rainfall over northwest and central India by forcing circulation patterns typically associated with circumglobal teleconnections (CGTs) which are strongest during the month of June. Interestingly, the influence of CGTs on rainfall in the northeast is opposite to its influence on other homogeneous regions, which explains the contrast in influence of the North Indian Ocean SSTs on rainfall over the northeast and over All India. The model representation of influence of forcings and strength of teleconnections is better for specific region–month pairs, which is seen to influence the monthly variations in skill of forecasting rainfall over homogeneous regions.

A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

AbstractAccurate ensemble forecasts of heavy precipitation in India are vital for many applications and essential for early warning of damaging flood events, especially during the monsoon season. In this study we investigate to what extent Quantile Mapping (QM) and Ensemble Model Output Statistics (EMOS) statistical postprocessing reduce errors in precipitation ensemble forecasts over India, in particular for heavy precipitation. Both methods are applied to day‐1 forecasts at 12‐km resolution from the 23‐member National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS‐G). By construction, QM leads to distributions close to the observed ones, while EMOS optimizes the ensemble spread, and it is not a priori clear which is better suited for practical applications. The methods are therefore compared with respect to several key aspects of the forecasts: local distributions, ensemble spread, and skill for forecasting precipitation amounts and the exceedance of heavy precipitation thresholds. The evaluation includes rank histograms, Continuous Ranked Probability Skill Scores (CRPSS), Brier Skill Scores (BSS), reliability diagrams, and receiver operating characteristic. EMOS performs best not only with respect to correcting under‐ or overdispersive ensembles, but also in terms of forecast skill for precipitation amounts and heavy precipitation events, with positive CRPSS and BSS in most regions (both up to about 0.4 in some areas), while QM in many regions performs worse than the raw forecast. QM performs best with respect to the overall local precipitation distributions. Which aspects of the forecasts are most relevant depends to some extent on how the forecasts are used. If the main criteria are the correction of under‐ or overdispersion, forecast reliability, match between the forecasted distribution for individual days and observations (CRPSS), and the skill in forecasting heavy‐precipitation events (BSS), then EMOS is the better choice for postprocessing NEPS‐G forecasts for short lead times.

Atmospheric kinetic energy spectra from global and regional NCMRWF unified modelling system

AbstractCharacteristics of the atmospheric kinetic energy (KE) spectrum follow a distinct power‐law dependence in the synoptic (k−3) and mesoscales (k−5/3), where k is the horizontal wave‐number. The representation of this canonical spectral behaviour provides a testimony of the acceptability of the design, configuration and performance of a numerical weather prediction (NWP) model. Therefore, the characteristics of the KE spectrum are studied using an operational version of the high‐resolution National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) system considering both global (∼12 km mesh) and regional (∼4 km mesh) configurations during the northern hemispheric summer period (June–August 2020). The synoptic‐scale spectral characteristics of the NCUM global (NCUM‐G) model match well with the observed spectrum in the tropics, midlatitudes and polar regions. However, the regional NCUM model (NCUM‐R) outperforms the NCUM‐G in representing the canonical slope at mesoscales. As the NCMRWF is accelerating towards a high‐resolution 6 km global (NCUM‐G6) operational model, the spectral characteristics from NCUM‐G6 are also studied during the monsoon period. Though the representation of the mesoscale spectrum is better in NCUM‐G6 relative to NCUM‐G, the regional model surpasses both the global models while demonstrating the mesoscale characteristics close to the observed spectral slopes. Further, the mesoscale spectrum deviates gradually from the Lindborg (1999) analytical fit in the mesoscales around 20Δx (where Δx ∼ 4 km), which specifies an effective resolution of the regional model. Hence, the regional model needs refinements optimally to further reduce the energy dissipation at high wave‐numbers to improve the mesoscale features at the limits of its grid resolution.

Systematic daytime increases in atmospheric biases linked to dry soils in irrigated areas in Indian operational forecasts

AbstractThe representation of land–atmosphere coupling in forecast models can significantly impact weather prediction. A previous case study in Northern India incorporating both model and observational data identified atmospheric biases in a high‐resolution forecast linked to soil moisture that impacted the representation of the monsoon trough, an important driver of regional rainfall. The aim of the current work is to understand whether this behavior is present in operational forecasts run by the India National Centre for Medium Range Weather Forecasting (NCMRWF). We utilize satellite observations and reanalysis to evaluate model fields in June, July, August, and September forecasts from 2020. Our analysis reveals systematic rapid growth in warm boundary layer biases during the daytime over North West India, which weaken overnight, consistent with excessive daytime surface sensible heat flux. The cumulative effect of these biases produces temperatures more than 4K warmer in 60‐h forecasts. These effects are enhanced by dry surface conditions. The biases impact circulation in the forecasts, which have implications for regional rainfall. The spatial distribution of warm biases in the Indo‐Gangetic Plain is remarkably consistent with the location of areas equipped for irrigation, a process that is not explicitly represented in the model. Our results provide compelling evidence that the development of an irrigation scheme within the model is needed to address the substantial forecast biases that we document.

Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India

Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result.

A multi-model ensemble tool for predicting districts level monsoon rainfall and extreme rainfall events over India

The southwest (SW) monsoon season from June to September (JJAS) is the major rainfall period over most parts of Indian regions. Accurate rainfall forecast is one of the most crucial and least predictable parameters of the numerical weather prediction (NWP) models because of its uneven distribution and patterns over the globe. During the last decade many studies have been carried out using different NWP models to predict rainfall incidents, and it is found that the forecast skill has been improved considerably. In the present study, a multi-model ensemble (MME) based tool has been developed for the prediction of SW monsoon rainfall at the district level over India for five days. The precipitation forecasts from five operational NWP modelling systems, viz., (i) Global Forecast System (GFS) and (ii) Global Ensemble Forecasting System (GEFS) running at India Meteorological Department, (iii) Global Forecast System model running at National Centre for Environment Prediction (NCEP), (iv) Unified Model running at National Centre for Medium-Range Weather Forecasting (NCMRWF) and (v) Global Spectral Model (GSM) running at Japan Meteorological Agency (JMA) have been used for developing the MME forecasts for SW monsoon 2021. The prediction skill of the MME and the individual model forecast is evaluated against observed district rainfall. The district-level heavy rainfall forecast from individual models and MME is also evaluated against the observed rainfall events useful for warning services. Different verification scores like Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Bias, Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Treat Score (ETS), Critical Success Index (CSI), etc. are calculated for the verification purpose. The different verification score shows that MME rainfall forecast has performed well than the individual models in different spatial domains and temporal scales. The CC between observed rainfall and day 1 MME forecast is 0.58, whereas GFS, GEFS, NCEP, NCUM and JMA are showing 0.43, 0.47, 0.49, 0.49 and 0.46 respectively. The RMSE observed for MME, GFS, GEFS, NCEP, NCUM and JMA are 12.7, 15.2, 14.1, 14.3, 16.6 and 14.1 mm/day respectively when compared with IMD observed rainfall. The inter-comparison of the model forecasts reveal that the MME method can generate skillful district rainfall forecast over India for operational use during the monsoon season.

Assimilation of aircraft observations over the Indian monsoon region: Investigation of the effects of COVID‐19 on a reanalysis

AbstractSince March 2020, the COVID‐19 pandemic has significantly reduced the availability of global aircraft‐based observations (ABOs), which has been restored later in 2021. This study focuses on the impact of ABOs on a regional reanalysis. Indian Monsoon Data Assimilation and Analysis (IMDAA) is a regional reanalysis for a period from 1979 to 2020 (originally up to 2018) over India and surrounding regions produced at the National Centre for Medium Range Weather Forecasting (NCMRWF), India, in collaboration with the UK Met Office. A comparison of the impact of ABOs on other conventional and satellite observations assimilated in the NCMRWF global model and IMDAA during 2019 and 2020 revealed the importance of ABOs, particularly in IMDAA, since it did not assimilate the latest satellite data as the IMDAA system was frozen in October 2016. A data denial experiment that removes all the ABOs from the IMDAA assimilation system for a period from March to November 2019 is designed. The results from the IMDAA reanalysis run, which assimilates ABOs during the same period, are compared with the data denial experiment. Assimilation of ABOs strengthened the upper tropospheric circulation, the Tropical Easterly Jet (TEJ), during the Indian summer monsoon compared to the data denial experiment. Analysis of the features of two cyclones that developed over the North Indian Ocean during the study period revealed that ABO assimilation played a key role in simulating the track and intensity of these cyclones when they were in the ‘severe’ category. Since the sample is small, more cyclone cases need to be analysed to consolidate the result.

Evaluation of precipitation forecasts for five-day streamflow forecasting in Narmada River basin

ABSTRACT The accuracy of quantitative rainfall forecasts (QPFs) obtained from numerical weather prediction (NWP) models plays a crucial role in setting up a catchment streamflow forecasting system. Additionally, a suitable hydrological model is required. This study addresses input and model uncertainty in developing a five-day streamflow forecasting system in Narmada River Basin. We use deterministic and ensemble QPFs obtained from the Japan Meteorological Agency (JMA), National Centre for Medium Range Weather Forecasting (NCMRWF), and European Centre for Medium-Range Weather Forecasts (ECMWF). We use two hydrological models, the Soil and Water Assessment Tool (SWAT) and variable infiltration capacity (VIC), to generate streamflow forecasts. By comparing simulated streamflow forecasts with the observed discharge data, our results indicate that the forecast accuracy of NCMRWF is better than other forecasting products for lead times of two to five days. The streamflow generated using VIC produces better results than that obtained from the SWAT hydrological model.

Using a Bayesian joint probability approach to improve the skill of medium-range forecasts of the Indian summer monsoon rainfall

Study regionGanga, Mahanadi, Godavari, Narmada, and Tapti River basins of India. Study focusThe manuscript focuses on improving skills of the Indian summer-monsoon precipitation forecasts obtained from National Center for Medium-Range Weather Forecasting (NCMRWF) at both sub-basin and gridded scale. A well-established Bayesian Joint Probability (BJP) based statistical post-processing approach, operational in Australia, is used for the first time in India throughout diverse geographical extent. The work evaluates how the post-processor can be used in a summer-monsoon dominated region like India. The study informs whether annual or seasonal precipitation forecasts should be used as the length of data will play crucial role in both the cases. The spread-skill of the ensembles obtained from BJP approach and the NCMRWF is explored. New hydrological insights for the regionIntroduction of the BJP-based post-processing approach in India could help the forecast community to implement more robust approach in improving the skills of the forecasts. Our results show that instead of using the data of whole year, only monsoonal precipitation forecasts are adequate to setup the BJP approach. The calibrated forecasts obtained using three years of hindcast and observations data at grids and at the centroid of 177 sub-basins are found to be more skillful. The calibrated forecasts can discriminate between extreme and low precipitation events, and have appropriate ensemble spread to capture precipitation peaks. This study presents a guideline for water managers and forecasters to apply BJP approach to improve the forecasts.

Spatial connections in extreme precipitation events obtained from NWP forecasts: A complex network approach

Understanding the spatial variability of extreme precipitation events (EPEs) has always been a challenging task, with climate change further complicating the issue. Many studies have looked into the spatiotemporal characteristics of EPEs and the possibility of them being connected. Most of these studies are based on observation data products which inform about the events after they have occurred. Various Quantitative Precipitation Forecasts (QPFs) obtained from the Numerical Weather Prediction (NWP) models such as those of the European Centre for Medium-range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centre for Medium-Range Weather Forecasting (NCMRWF), and UK Met Office (UKMO) are available which inform about the precipitation events before they have occurred. In this study, we inter-evaluate four gridded (deterministic) QPFs over the Ganga River basin of India for their ability to detect the spatial connections among EPEs during the monsoon months, i.e., June, July, August and September (JJAS). Several experimental runs are performed in multiple time periods (13, 6 and 4 years) at various percentile thresholds (85th, 90th and 95th percentile) at two spatial resolutions of 25 and 50-km. Moreover, we also evaluate the forecast patterns of the QPF obtained from the national agency of India, i.e., NCMRWF and analyse its performance with respect to the other three selected QPFs from international agencies. For the observation/reference dataset, the gridded Indian Meteorological Department (IMD) dataset is selected. We first use the deterministic and dichotomous error statistics to evaluate the performance of NWP forecasts. Further, we employ the theory of complex networks to create networks of extreme precipitation at each grid. With this work, we aim to answer (a) can the theory of complex networks be a feasible tool for evaluating the performance of NWP forecasts in detecting extreme precipitation behaviour; (b) does the change in temporal length of the NWP forecasts influence the spatial structure of extreme precipitation as observed before, and (c) can a global NWP model, larger in length, be used as a potential substitute to the NCMRWF model. The ECMWF model performs well with respect to IMD as the average CC for TP_13 is 0.42 while they are 0.31 and 0.34 for TP_6 and TP_4, respectively. Also, we find that ECMWF can be a suitable substitute for the NCMRWF model. The identification of a substitute precipitation forecast, larger in temporal length, can be beneficial as input in hydrological models for streamflow forecasting and estimating parameters for bias correction of precipitation, among others.

Contribution of lagged members to the performance of a global ensemble prediction system

National Centre for Medium Range Weather Forecasting (NCMRWF), India runs operationally a global ensemble prediction system (NEPS-G) which has 23 ensemble members. The control and 11 perturbed ensemble members have the initial conditions of 00 UTC. The start time of the other 11 perturbed members is 12 UTC of the previous day. The objective of the present paper is to study the effect of the lagged ensemble members on the forecast quality of NEPS-G. For this, we have carried out an inter-comparison of the relative skills of (i) 11-member ensemble starting from 00 UTC i.e., E00_11, (ii) 11 lagged ensemble members starting from 12 UTC i.e., E12_11 and the (iii) operational 23-member ensemble i.e. E23 using the verification of temperature, wind and geopotential height forecasts for 7-day forecast lead times for April–May-June 2019 (AMJ) of summer and December 2019–January 2020 (DJ) of winter. The temperature at 850 hPa and geopotential height at 500 hPa are verified over the northern hemisphere. The zonal and meridional winds at 850 hPa and 200 hPa are verified over the region 0oN-40oN, 60°E-110°E. The metrics used for validation are ensemble-spread and root-mean-square error (RMSE) relationship, brier skill score (BSS), reliability diagram, outliers statistics, relative operating characteristics (ROC) score and ranked probability score (RPS). For brevity, only the results for Z500, T850 and U850 have been discussed in detail here.The results show that in both seasons, the RMSE-spread relationship is better for E23 than for E00_11 and E12_11. For T850 and Z500 over NH, both E23 and E00_11 have higher RMSE and higher ensemble spread in DJ season as compared to AMJ season. In case of wind over the Indian region, both RMSE and ensemble spread exhibit lower values during DJ as compared to AMJ for both ensemble configurations. For both seasons, the BSS of E23 is positive and the ROC score is >0.5 beyond 7 days forecast times for all the variables for all three ensembles and the percentage of outliers in E23 is also less. Overall, the contribution of 11 lagged ensemble members improves the skill of NEPS-G in both seasons and skill is better during winter (DJ season). The error in the mean forecast track for tropical cyclone ‘Fani’ for E23 is found to be less than for E00_11.We have also compared the forecast skill of E23 with that of the ensemble formed by 22 members, all running from 00 UTC i.e. E00_22 for the same set of variables as well as precipitation for one month forecast data from each season, namely June 2019 from summer and January 2020 from winter. Results show that the RMSE-spread relationship is very much similar for both configurations and both seasons. For E00_22 and E23, the ensemble spread is closer to the RMSE of ensemble mean for all the variables in January compared to June. BSS was slightly higher for E00_22 but was not significant at all lead time for all the variables except Z500 in January which shows significant improvement in BSS of E00_22 from day 2 to day 5. In precipitation forecast verification, RPS for E00_22 is slightly less (though significant) than E23 at all forecast lead times in June while in January there was no significant difference. So, E23 dataset of NEPS-G which demands less computational resources at the same time offers more skilful forecast than E00_11 over a longer forecast lead time and is nearly as skilful as E00_22.

Evaluation of NCMRWF numerical weather prediction models for SHAR region

National Centre for Medium Range Weather Forecasting (NCMRWF) generates the weather forecasts from global/ regional models, giving an opportunity for demonstrating different applications of these forecasts for various strategic needs of India. Sriharikota Range (SHAR), is a satellite launching center of India, functioning under the Indian Space Research Organization (ISRO). Weather forecasts are very crucial for space launch operations. An accurate and on-time weather prediction at the launching site is highly needed during various stages, which plays a critical role in taking the “Go/ No Go” decision. During launch periods, weather forecasts over the SHAR region are prepared from the NCMRWF models with a special emphasis and shared them with ISRO on real time basis. In this paper, these forecasts are validated through observations at Chennai in the best possible way. The various skills are evaluated during different seasons, the products are found practically useful for SHAR launching projects in recent times. The results show that model variables of geopotential height, relative humidity, zonal and meridional winds agree with observations at SHAR, and 72 h forecast has better agreement.

Quantile mapping bias correction methods to IMDAA reanalysis for calibrating NCMRWF unified model operational forecasts

ABSTRACT This study focuses on assessing different quantile mapping (QM) bias correction approaches based on empirical and parametric methods to bias-correct the Indian Monsoon Data Assimilation and Analysis (IMDAA) precipitation data and subsequently calibrate the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model operational forecasts. The two coastal cities Chennai and Mumbai, India, are chosen here to support the Integrated Flood Warning System (IFLOWS), initiated by the Ministry of Earth Sciences, Government of India, which provides early warning and decision support during flooding. The empirical QM methods are relatively better in correcting the quantiles with calibrated precipitation close to the observed cumulative distribution at these coastal cities. However, in extreme rainfall cases, the skill of calibrated precipitation through parametric methods seems promising and suitable for flood forecasting applications. Hence, this study demonstrates QM methods’ performance and their potential in downscaling precipitation that has significant implications for urban flood models.

Skill of the extended range prediction (NERP) for Indian summer monsoon rainfall with NCMRWF global coupled modelling system

AbstractAn extended range prediction system (NERP) based on the Unified global coupled modelling system has been implemented at National Centre for Medium Range Weather Forecasting (NCMRWF). Predictions of weekly anomalies for rainfall, winds and surface temperature are issued once a week for the subsequent 4 weeks. This study is an assessment of the skill of the NERP system over India during the summer monsoon period of June to September (JJAS). The week‐1 and week‐2 rainfall anomaly forecasts exhibit good prediction skill over the Indian region. The evaluations made on meteorologically homogeneous regions of India show moderate to good skill even in week‐3 and week‐4 forecasts for some regions. The variability of the monsoon seasonal cycle in the model is well represented along with the magnitude of summer monsoon rainfall. The model brings out the mean monsoon rainfall features – the peaks during JJAS occurring over the Western Ghats, the Arakan coast and the Bay of Bengal. The onset of the monsoon and the interannual variability of the northward propagation of the rainfall anomalies over the Indian longitudes match reasonably well with observations. The model has some wet bias over the equatorial Indian Ocean and mild dry bias over the Indian land region that develop during the 4‐week period. The anomaly correlations of rainfall are significant over most of the subdivisions of India till week‐3 and week‐4. Probabilistic scores of reliability Receiver Operating Characteristic (ROC) and fair continuous ranked probability score (FCRPS) show that the model exhibits good skill over most regions till week‐2 and moderate skill in weeks 3–4 over other regions. The estimates of forecast reliability are low, a likely consequence of the limited ensemble size. A case‐study of year 2018 showed that the JJAS rainfall regime shifts from wet to dry and vice versa are captured well.

Development of early sea surface temperature biases in the tropical Indian Ocean in a coupled model

As part of seamless modeling strategy the ocean is increasingly becoming an important component of weather and climate forecasting systems across time scales. While the skill of sea surface temperatures (SSTs) at long-range forecasts is widely documented, few studies exist on the representation of evolving SST biases at shorter time-scales in a global coupled model. In this study, daily initialized runs of National Centre for Medium Range Weather Forecasting (NCMRWF) coupled model are analyzed over several seasons for up to two weeks of leadtimes. Analysis of source of SST biases at shorter time scales is carried out. It is found that the model captures well the spatial structure of the SSTs and their seasonal cycle. The SST correlations averaged over different regions are over 0.8 at day-14 leadtime, and RMSEs remain less than 0.6 °C over all regions except the south east equatorial Indian Ocean (SEEIO). SEEIO shows lowest of the correlations and highest of the biases among the regions considered. The difference in the skill of the model over different regions is found to be related to the representation of SST variability over the region. An assessment of the biases in different seasons suggests that the day-1 biases are related to the errors in the ocean state used to initialize the model. However, by day-14 increase in biases are seen over the regions where these are found to be related to the wind biases. Highly significant correlation (r=−0.39) is seen between winds and SST biases. Errors in winds also affect both the surface heat fluxes and the upper ocean mixing: wind biases are positively correlated to both latent heat fluxes and mixed layer depths (MLDs). It is found that the errors in atmospheric winds drive the biases in SSTs by influencing the upper ocean processes. An analysis of the relationship between latent heat fluxes and SSTs shows that the air-sea interactions are realistically represented in the model. This gives us confidence in attributing the SST biases to the upper ocean parameters. Contributions of different parameters to the SST biases are estimated using the heat budget of the upper ocean. It is shown that errors in LH fluxes and upper-ocean mixing explain large part of biases in SST tendencies. However, the errors in upper-ocean mixing explain much higher variance of biases in SST tendencies as compared to LH fluxes. This is found to be related to stronger dependence of SST tendencies to the MLD tendencies in the model. Identification of the sources of errors will help in our understanding of the biases at longer time scales and provides feedback for further model development.

Evaluation of quantitative precipitation forecast in five Indian river basins

ABSTRACT Quantitative precipitation forecast (QPF) obtained from a numerical weather prediction model is used for streamflow forecasting. This study systematically evaluates the performance of a deterministic QPF obtained from a national agency, the National Centre for Medium Range Weather Forecasting (NCMRWF), compared with that obtained from three international agencies. For observation/reference datasets, we used satellite, raingauge, and satellite-gauge merged data products. The forecast skill is evaluated for 177 sub-basins in Ganga, Narmada, Mahanadi, Tapti, and Godavari river basins. Our results indicate that the forecast accuracy of NCMRWF is closely comparable with that of QPF obtained from a European agency. We conclude that selecting accurate observation/reference data is critical for forecast performance evaluation. We determine the preferred forecast and reference datasets in the selected river basins. Our results suggest that for estimating the parameters of a post-processor, a comparable dataset can be used for which data are available for a longer duration.

Tropical Cyclone Heat Potential (TCHP) from the NCMRWF NEMO based global ocean analysis and forecast system

In this study, the Nucleus European Modelling of Ocean (NEMO) based global ocean analysis and forecast system configured at National Centre for Medium Range Weather Forecasting (NCMWRF) is used to compute the upper ocean heat content up to 26° isotherm depth called as Tropical Cyclone Heat Potential (TCHP). NCMRWF has the real-time TCHP monitoring capability on daily basis. It is produced in real time using the global ocean forecast system up to 10 days using the ocean only model and up to 15 days using the coupled atmosphere-ocean model for monitoring the upper ocean and also for research purpose mainly for Tropical Cyclone (TC) study. Four pre-monsoon TCs with category above cyclonic storms in which two from Arabian Sea and two from Bay of Bengal are also examined. The Sea Surface Temperature drop is much more over the regions where the surface wind stress anomaly is strong. In both regions, various small scale warm and cold eddies are observed during TCs. TCHP anomaly is positive/negative over the region where Sea Surface Height (SSH) anomaly is positive/negative. From TCHP, it is manifest that the magnitude of TCHP anomaly is higher in BoB than AS region during TCs.

Delhi Model with Chemistry and aerosol framework (DM‐Chem) for high‐resolution fog forecasting

AbstractWe introduce here the Delhi Model with Chemistry and aerosol framework (DM‐Chem), a high‐resolution (330 m) visibility and fog forecasting model with the UK Chemistry and Aerosol scheme. DM‐Chem is developed at the National Centre for Medium‐Range Weather Forecasting (NCMRWF) in collaboration with UK Met Office and Carnegie Mellon University. We present a revised visibility scheme here which uses prognostic aerosols (mass and number concentration) from the two‐moment prognostic Global Model of Aerosol Processes (GLOMAP) ‐mode aerosol scheme. This scheme represents aerosol microphysical processes and the internal mixing of chemical components within five log‐normal size modes. The cloud microphysics parameterization also makes use of the GLOMAP scheme to predict cloud droplet number concentration (). Therefore we are able to evaluate the performance of the scheme indirectly by comparing the predicted with that derived from Moderate‐Resolution Imaging Spectroradiometer (MODIS) retrievals over the Indo‐Gangetic Plain. Aerosol emissions for the inner (330 m) nest are prescribed with a high‐resolution inventory for Delhi, from the System of Air Quality and Weather Forecasting project (SAFAR). Winter‐time stubble burning is represented using the MODIS‐based Fire Energetics and Emissions Research inventory, updated daily. In this article we consider case studies spanning both foggy and haze days of winter 2019–2020. The model compares well with the observed visibility and its spatial and diurnal patterns. The SAFAR emissions help improve the near‐surface model fields. For the “haze” case, the single scattering albedo (SSA) is high along the regions of high AOD, and the surface radiative cooling is enhanced. During the “foggy” case, the SSA is not clearly related to AOD, but aerosol absorption does lead to a minor surface warming via the semi‐direct effect at the time of onset of fog. The DM‐Chem entered operational mode at NCMRWF from winter 2020 and provides high‐resolution visibility forecast for Delhi and the adjoining region.