John P. George

All-sky radiance simulation of Megha-Tropiques SAPHIR microwave sensor using multiple scattering radiative transfer model for data assimilation applications

Incorporation of cloud- and precipitation-affected radiances from microwave satellite sensors in data assimilation system has a great potential in improving the accuracy of numerical model forecasts over the regions of high impact weather. By employing the multiple scattering radiative transfer model RTTOV-SCATT, all-sky radiance (clear sky and cloudy sky) simulation has been performed for six channel microwave SAPHIR (Sounder for Atmospheric Profiling of Humidity in the Inter-tropics by Radiometry) sensors of Megha-Tropiques (MT) satellite. To investigate the importance of cloud-affected radiance data in severe weather conditions, all-sky radiance simulation is carried out for the severe cyclonic storm ‘Hudhud’ formed over Bay of Bengal. Hydrometeors from NCMRWF unified model (NCUM) forecasts are used as input to the RTTOV model to simulate cloud-affected SAPHIR radiances. Horizontal and vertical distribution of all-sky simulated radiances agrees reasonably well with the SAPHIR observed radiances over cloudy regions during different stages of cyclone development. Simulated brightness temperatures of six SAPHIR channels indicate that the three dimensional humidity structure of tropical cyclone is well represented in all-sky computations. Improved correlation and reduced bias and root mean square error against SAPHIR observations are apparent. Probability distribution functions reveal that all-sky simulations are able to produce the cloud-affected lower brightness temperatures associated with cloudy regions. The density scatter plots infer that all-sky radiances are more consistent with observed radiances. Correlation between different types of hydrometeors and simulated brightness temperatures at respective atmospheric levels highlights the significance of inclusion of scattering effects from different hydrometeors in simulating the cloud-affected radiances in all-sky simulations. The results are promising and suggest that the inclusion of multiple scattering radiative transfer models into data assimilation system can simulate the cloud-affected microwave radiance data which provide detailed information on three dimensional humidity structure of the atmosphere in the presence of cloud hydrometeors.

Simulation of Tropical Cyclones over Bay of Bengal with NCMRWF Regional Unified Model

This study delineates the relative performance of the 12-km resolution NCMRWF regional Unified Model (NCUM-R) over the operational global NCUM (NCUM-G) model. Forecasts of four Bay of Bengal (BoB) landfalling tropical cyclones (TCs) using several different initial conditions (ICs) are used to compare the performance of two models. The position and intensity errors of the TCs are estimated with respect to the India Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) best-track datasets and an inter-comparison study is also carried out between IMD and JTWC. The overall results suggest that the NCUM-R simulates the position and intensity of TCs more accurately compared to the NCUM-G. A majority of the TC tracks in the NCUM-G diverge more from the IMD track when compared to NCUM-R simulated tracks. It is also clearly noticed that both the models are more skillful in track prediction when initialized at intensity stages greater than “cyclone” category. However, the mean position errors at different forecast hours and landfall errors of TCs are reduced by approximately 31 and 47% in the NCUM-R simulations compared to NCUM-G simulations, respectively. The mean gain in skill of the NCUM-R in cross track (CT) and along track (AT) error is around 29 and 24% over NCUM-G, respectively. The intensity errors are less in the NCUM-R simulations. The mean rainfall skill scores are considerably improved in the NCUM-R simulations in day-1 and day-2 as compared to the NCUM-G simulations. It is noticed that the mean position errors of the TCs are approximately 8% lower when compared against the JTWC tracks than the IMD tracks. However, the intensity errors are higher against the JTWC than that of IMD most likely due to the averaging period of the wind speed.

Quality assessment of VVP winds from Indian Doppler weather radars: a data assimilation perspective

Abstract. This study demonstrates the data assimilation perspective of vertical profiles of winds derived from the Doppler weather radar (DWR) data using the volume velocity processing (VVP) technique. The VVP information from the nine Indian DWR stations is used in this study. The winds from the Indian DWR network are assessed for their quality based on the National Center for Medium Range Weather Forecasting unified model (NCUM). This paper describes the quality of DWR VVP winds, preprocessing of VVP wind data, and their use in NCUM 4 D-Var assimilation systems. The VVP winds are compared against an NCUM background (short forecast) to understand the observation bias. Comparison of VVP winds is also made with colocated radiosonde wind observations. The VVP winds show less bias when compared against model background especially in the region of strong wind flow. The correlation between the observations and the model background is greater than 0.7 for most of the radars. The VVP winds provide reasonably accurate estimates of the vertical wind structure in the troposphere over radar locations, which can be effectively used in the numerical weather prediction system.

Effect of new radiance observations on numerical weather prediction models

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 Figure 1. Innovations (differences between the observations and simulations) for the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) channel 1. Results are shown with (blue curve) and without (black curve) bias correction.

Assimilation of CrIS Hyperspectral Radiances in a 4D-Var Assimilation System

This study demonstrates the advantage of the assimilation of Cross-track Infrared Sounder (CrIS) radiances of the Suomi-NPP satellite observation using 4D-Var assimilation system with global NCMRWF Unified Model (NCUM). The observation pre-processing system, quality control and thinning strategy for CrIS observations in addition to the impact of this observation in the analysis also discussed. Observation bias statistics are computed against the NCUM model fields from a short-range forecast (background) for quality control. The impact on forecasts is evaluated using “Observing System Simulation Experiments (OSSEs)”. The combined effect of hyperspectral and microwave radicalizes. The results show that CrIS data reduces the total number of observations and increases the RMS values for hyperspectral radiances.

Impact of AIRS radiance in the NCUM 4D-VAR assimilation system

The hyperspectral radiances from Atmospheric InfraRed Sounder (AIRS), on board NASA-AQUA satellite, have been processed through the Observation Processing System (OPS) and assimilated in the Variational Assimilation (VAR) System of NCMRWF Unified Model (NCUM). Numerical experiments are conducted in order to study the impact of the AIRS radiance in the NCUM analysis and forecast system. NCMRWF receives AIRS radiance from EUMETCAST through MOSDAC. AIRS is a grating spectrometer having 2378 channels covering the thermal infrared spectrum between 3 and 15 μm. Out of 2378 channels, 324 channels are selected for assimilation according to the peaking of weighting function and meteorological importance. According to the surface type and day-night conditions, some of the channels are not assimilated in the VAR. Observation Simulation Experiments (OSEs) are conducted for a period of 15 days to see the impact of AIRS radiances in NCUM. Statistical parameters like bias and RMSE are calculated to see the real impact of AIRS radiances in the assimilation system. Assimilation of AIRS in the NCUM system reduced the bias and RMSE in the radiances from instruments onboard other satellites. The impact of AIRS is clearly seen in the hyperspectral radiances like IASI and CrIS and also in infrared (HIRS) and microwave (AMSU, ATMS, etc.) sensors.

Assimilation of SAPHIR radiance: impact on hyperspectral radiances in 4D-VAR

Assimilation of a new observation dataset in an NWP system may affect the quality of an existing observation data set against the model background (short forecast), which in-turn influence the use of an existing observation in the NWP system. Effect of the use of one data set on the use of another data set can be quantified as positive, negative or neutral. Impact of the addition of new dataset is defined as positive if the number of assimilated observations of an existing type of observation increases, and bias and standard deviation decreases compared to the control (without the new dataset) experiment. Recently a new dataset, Megha Tropiques SAPHIR radiances, which provides atmospheric humidity information, is added in the Unified Model 4D-VAR assimilation system. In this paper we discuss the impact of SAPHIR on the assimilation of hyper-spectral radiances like AIRS, IASI and CrIS. Though SAPHIR is a Microwave instrument, its impact can be clearly seen in the use of hyper-spectral radiances in the 4D-VAR data assimilation systems in addition to other Microwave and InfraRed observation. SAPHIR assimilation decreased the standard deviation of the spectral channels of wave number from 650 -1600 cm-1 in all the three hyperspectral radiances. Similar impact on the hyperspectral radiances can be seen due to the assimilation of other Microwave radiances like from AMSR2 and SSMIS Imager.

IASI hyperspectral radiances in the NCMRWF 4D-VAR assimilation system: OSE

Accuracy of global NWP depends more on the contribution of satellite data than the surface based observations. This is achieved through the better usage of satellite data within the data assimilation system. Efforts are going on at NCMRWF to add more and more satellite data in the assimilation system both from Indian and international satellites in geostationary and polar orbits. Impact of the new dataset is assessed through Observation System Experiments (OSEs), through which the impact of the data is evaluated comparing the forecast output with that of a control run. This paper discusses one such OSEs with Infrared Atmospheric Sounder Interferometer (IASI) onboard MetOp-A and B. IASI is the main payload instrument for the purpose of supporting NWP. IASI provides information on the vertical structure of the atmospheric temperature and humidity with an accuracy of 1K and a vertical resolution of 1 km, which is necessary to improve NWP. IASI measures the radiance emitted from the Earth in 8641 channels, covering the spectral interval 645-2760 cm-1. The high volume data resulting from IASI presents many challenges, particularly in the area of assimilation. Out of these 8641 channels, 314 channels are selected depending on the relevance of information in each channel to assimilate in the NCMRWF 4D-VAR assimilation system. Studies show that the use of IASI data in NWP accounts for 40% of the impact of all satellite observations in the NWP forecasts, especially microwave and hyperspectral infrared sounding techniques are found to give the largest impacts

Verification of dust forecast over the Indian region with satellite and ground based observations

ABSTRACT This article presents the verification results of the dust forecast by a numerical model over India and neighbouring regions. National Centre for Medium Range Weather Forecasting Unified Model (NCUM) is a global numerical weather prediction (NWP) model with a prognostic dust scheme. Evaluation of the performance of dust forecast by NCUM is carried out in this study. Model forecast of dust optical depth (DOD) at 550 nm is validated against ground-based and satellite observations since optical depth measurements in mid-visible wavelength are easily available. Daily 5-day forecast based on 00 UTC initial condition during dust dominated pre-monsoon season (April–May) of 2014 is used in this study. Location specific and geographical distribution of dust forecast is validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite retrieved DOD observation at 532 nm, Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), Ozone Monitoring Instrument (OMI), aerosol index, and Aerosol Robotic Network (AERONET) station data of total and coarse mode AOD. The verification results indicate that NCUM dust forecast generally gives good representation of large scale geographical distribution of dust over the western region of India. DOD forecasts show good correlation with co-located CALIPSO DOD over the western part (0.71) compared to central (0.58) and eastern (0.61) part of India in April while it show similar trend in May with slightly improved correlation (0.68) over the eastern part of India. Results also show that DOD forecasts are better correlated to AERONET coarse mode AOD observations over Jaipur in April and over Kanpur in May. Vertical distribution of dust concentrations in the forecast show reasonably good agreement with attenuated backscatter and depolarization ratio from CALIPSO observations. The model is also able to simulate spatiotemporal distribution of dust during a major dust event as observed by CALIPSO, MODIS, and OMI.

Improved Track and Intensity Predictions Using Cyclone Bogusing and Regional Assimilation

Tropical cyclones (TC) originate and intensify over the oceans where data coverage is sparse. This leads to inaccurate representation of location and intensity of tropical cyclones in the initial condition (IC) of the NWP models; one of the reasons for large errors in the forecast track and intensity. These errors are reduced by use of bogus vortex in the initial condition (Trinh and Krishnamurti, 1992; Kurihara et al., 1993; Leslie and Holland, 1995). Kurihara et al. (1993) proposed a scheme to improve the representation of a TC in the IC of a high-resolution hurricane model. Satellite data coverage over the ocean along with high resolution data assimilation tools (3DVAR or 4DVAR) also provides an opportunity to improve the IC. This study concentrates on the impact of bogus vortex and regional assimilation in WRF model on track predictions of some of the 2010 TCs of North Indian Ocean.