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A method to assimilate all-sky radiances from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was developed within the Weather Research and Forecasting (WRF) model's data assimilation (WRFDA) system. The four essential elements are: (1) extending the community radiative transform model's (CRTM) interface to include hydrometeor profiles; (2) using total water Q t as the moisture control variable; (3) using a warm-rain physics scheme for partitioning the Q t increment into individual increments of water vapour, cloud liquid water and rain; and (4) adopting a symmetric observation error model for all-sky radiance assimilation. Compared to a benchmark experiment with no AMSR2 data, the impact of assimilating clear-sky or allsky AMSR2 radiances on the analysis and forecast of Hurricane Sandy (2012) was assessed through analysis/ forecast cycling experiments using WRF and WRFDA's three-dimensional variational (3DVAR) data assimilation scheme. With more cloud/precipitation-affected data being assimilated around tropical cyclone (TC) core areas in the all-sky AMSR2 assimilation experiment, better analyses were obtained in terms of the TC's central sea level pressure (CSLP), warm-core structure and cloud distribution. Substantial (!20 %) error reduction in track and CSLP forecasts was achieved from both clear-sky and all-sky AMSR2 assimilation experiments, and this improvement was consistent from the analysis time to 72-h forecasts. Moreover, the allsky assimilation experiment consistently yielded better track and CSLP forecasts than the clear-sky did for all forecast lead times, due to a better analysis in the TC core areas. Positive forecast impact from assimilating AMSR2 radiances is also seen when verified against the European Center for Medium-Range Weather Forecasts (ECMWF) analysis and the Stage IV precipitation analysis, with an overall larger positive impact from the all-sky assimilation experiment.

For variational data assimilation, the background error covariance matrix plays a crucial role because it is strongly linked with the local meteorological features, and is especially dominated by error correlations between different analysis variables. Multivariate background error (MBE) statistics have been generated for two regions, namely the Tropics (covering Indonesia and its neighborhood) and the Arctic (covering high latitudes). Detailed investigation has been carried out for these MBE statistics to understand the physical processes leading to the balance (defined by the forecasts error correlations) characteristics between mass and wind fields for the low and high latitudes represented by these two regions. It is found that in tropical regions, the unbalanced (full balanced) part of the velocity potential (divergent part of wind) contributes more to the balanced part of the temperature, relative humidity, and surface pressure fields as compared with the stream function (rotational part of wind). However, the exact opposite happens in the Arctic. For both regions, the unbalanced part of the temperature field is the main contributor to the balanced part of the relative humidity field. Results of single observation tests and six-hourly data assimilation cycling experiments are consistent with the respective balance part contributions of different fields in the two regions. This study provides an understanding of the contrasting dynamical balance relationship that exists between the mass and wind fields in high-and low-latitude regions. The study also examines the impact of MBE on Weather Research and Forecasting model forecasts for the two regions.

The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission was launched in April 2006. As part of its mission, COSMIC will provide approximately 2500–3000 global positioning system (GPS) radio occultation (RO) soundings per day distributed uniformly around the globe. In this study, a series of sensitivity experiments are conducted to assess the potential impact of COSMIC GPS RO data on the regional weather analysis over the Antarctic. Soundings of refractivity are assimilated into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model using its three-dimensional variational data assimilation system. First, the sensitivity of the analysis to the background error statistics and balance constraints is analyzed. Then the effects of the data distribution and the observational error of the simulated refractivity observations are examined. In this study, the simulated soundings are based on a realistic set of orbit parameters of the COSMIC constellation. Analysis of the assimilation results indicates the significant potential impact of COSMIC data on regional analyses over the Antarctic. In the one case studied here, the root-mean-square differences between the background and observed values are reduced by 12% in the horizontal wind component, 17% in the temperature variable, 8% in the specific humidity, and 22% in the pressure field when COSMIC GPS RO data are assimilated into the system by using a 6-h assimilation time window. These preliminary results suggest that COSMIC GPS RO data can have a significant impact on operational numerical weather analysis in the Antarctic.

This work is a first assessment of utilizing Doppler Weather Radar (DWR) radial velocity and reflectivity in a mesoscale model for prediction of Bay of Bengal monsoon depressions (MDs). The Weather Research Forecasting (WRF) modelling system -Advanced Research version (ARW) is customized and evaluated for the Indian monsoon region by generating domain-specific Background Error (BE) statistics and experiments involving two assimilation strategies (cold start and cycling). The monthly averaged 24 h forecast errors for wind, temperature and moisture profiles were analysed. From the statistical skill scores, it is concluded that the cycling mode assimilation enhanced the performance of the WRF threedimensional variational data assimilation (3DVAR) system over the Indian region using conventional and non-conventional observations. DWR data from a coastal site were assimilated for simulation of two different summer MDs over India using the WRF-3DVAR analysis system. Three numerical experiments (control without any Global Telecommunication System (GTS) data, with GTS, and GTS as well as DWR) were performed for simulating these extreme weather events to study the impact of DWR data.The results show that even though MDs are large synoptic systems, assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. All aspects of the MD simulations such as mean-sea-level pressure, winds, vertical structure and the track are significantly improved due to the DWR assimilation. Study results provide a positive proof of concept that the assimilation of the Indian DWR data within WRF can help improve the simulation of intense convective systems influencing the large-scale monsoonal flow.

The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia's WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS.

There is an urgent need to increase the capacity of developing countries to take part in the study and monitoring of their environments through remote sensing and spacebased Earth observation technologies. The Open Data Cube (ODC) provides a mechanism for efficient storage and a powerful framework for processing and analyzing satellite data. While this is ideal for scientific research, the expansive feature space can also be daunting for end-users and decision-makers who simply require a solution which provides easy exploration, analysis, and visualization of Analysis Ready Data (ARD). Utilizing innovative webdesign and a modular architecture, the Committee on Earth Observation Satellites (CEOS) has created a web-based user interface (UI) which harnesses the power of the ODC yet provides a simple and familiar user experience: the CEOS Data Cube (CDC). This paper presents an overview of the CDC architecture and the salient features of the UI. In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The fully-customizable source code of the UI is available at our public repository. Interested parties can download the source and build their own UIs. The UI empowers users by providing features that assist with streamlining data preparation, data processing, data visualization, and sub-setting ARD products in order to achieve a wide variety of Earth imaging objectives through an easy to use web interface.