scholarly journals Assessment of Cloud Retrieval for IASI 1D-Var Cloudy-Sky Assimilation and Improvement with an ANN Approach

2020 ◽  
Vol 35 (4) ◽  
pp. 1363-1380
Author(s):  
Ahreum Lee ◽  
Byung-Ju Sohn ◽  
Ed Pavelin ◽  
Yoonjae Kim ◽  
Hyun-Suk Kang ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Model Simulation ◽  
Cloud Fraction ◽  
Computational Time ◽  
Var Analysis ◽  
Minimum Residual ◽  
Forecasting System ◽  
Infrared Radiances ◽  
Artificial Neural Network Ann ◽  
Assimilation System

AbstractThe Unified Model (UM) data assimilation system incorporates a 1D-Var analysis of cloud variables for assimilating hyperspectral infrared radiances. For the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation, a first guess of cloud top pressure (CTP) and cloud fraction (CF) is estimated using the minimum residual (MR) method, which simultaneously obtains CTP and CF by minimizing radiance difference between observation and model simulation. In this study, we examined how those MR-based cloud retrievals behave, using “optimum” CTP and CF that yield the best 1D-Var analysis results. It is noted that the MR method tends to overestimate cloud top height while underestimating cloud fraction, compared to the optimum results, necessitating an improved cloud retrieval. An artificial neural network (ANN) approach was taken to estimate CTP as close as possible to the optimum value, based on the hypothesis that CTP and CF closer to the optimum values will bring in better 1D-Var results. The ANN-based cloud retrievals indicated that CTP and CF biases shown in the MR method are much reduced, giving better 1D-Var analysis results. Furthermore, the computational time can be substantially reduced by the ANN method, compared to the MR method. The evaluation of the ANN method in a global weather forecasting system demonstrated that it helps to use more temperature channels in the assimilation, although its impact on UM forecasts was found to be near neutral. It is suggested that the neutral impact may be improved when error covariances for the cloudy sky are employed in the UM assimilation system.

scholarly journals Multivariate Minimum Residual Method for Cloud Retrieval. Part II: Real Observations Experiments

2014 ◽  
Vol 142 (12) ◽  
pp. 4399-4415 ◽  
Author(s):  
Thomas Auligné
Keyword(s):  
Cloud Fraction ◽  
Cloud Detection ◽  
Atmospheric Infrared Sounder ◽  
Detection Scheme ◽  
Weather Forecasts ◽  
Cloud Systems ◽  
Medium Range ◽  
Minimum Residual ◽  
Infrared Radiances ◽  
High Chance

Abstract In Part I of this two-part paper, the multivariate minimum residual (MMR) scheme was introduced to retrieve profiles of cloud fraction from satellite infrared radiances and identify clear observations. In this paper it is now validated with real observations from the Atmospheric Infrared Sounder (AIRS) instrument. This new method is compared with the cloud detection scheme presented earlier by McNally and Watts and operational at the European Centre for Medium-Range Weather Forecasts (ECMWF). Cloud-top pressures derived from both algorithms are comparable, with some differences at the edges of the synoptic cloud systems. The population of channels considered as clear is less contaminated with residual cloud for the MMR scheme. Further procedures, based on the formulation of the variational quality control, can be applied during the variational analysis to reduce the weight of observations that have a high chance of being contaminated by cloud. Finally, the MMR scheme can be used as a preprocessing step to improve the assimilation of cloud-affected infrared radiances.

Catastrophic Effects of Floods on Environment and Health: Evidence from Pakistan

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Falak Shad Memon ◽  
M. Yousuf Sharjeel
Keyword(s):  
Paradigm Shift ◽  
Weather Forecasting ◽  
Human Life ◽  
National Level ◽  
Operational Cost ◽  
Site Assessment ◽  
Environment And Health ◽  
Forecasting System ◽  
Frame Work ◽  
Multiple Levels

<span>Torrential rains and floods have been causing irreplaceable losses to both human lives and environment in <span>Pakistan. This loss has reached to an extent of assively aggrieved situation to reinstate life at <span>operationally viable position. This paper unfolds the notion that only constructive paradigm shift to <span>overcome this phenomenon is vital as a strategy. Multiple levels of observations and on-site assessment <span>of various calamity-prone venues were considered to probe into this scenario. Some of the grave site in <span>Sindh and Punjab were observed and necessarily practicable measures were recommended to avoid loss to <span>human health and environment. The paper finds that a consistent drastic management authority on <span>national level with appropriate caliber and forecasting expertise can reduce the damage to human life and <span>environment to great extent. Weather forecasting system need to be installed at many appropriately <span>observed cities and towns in the country with adequate man power, funds and technical recourses. By <span>implementing the proper frame work of prevention and mitigation of floods country can save the major <span>costs cleanup and recovery. These measures are expected to reduce operational cost of state in terms of <span>GDP and GNP to restore life and environment.</span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

scholarly journals Improvements in medium range weather forecasting system of India

2014 ◽  
Vol 123 (2) ◽  
pp. 247-258 ◽  
Author(s):  
V S PRASAD ◽  
SAJI MOHANDAS ◽  
SURYA KANTI DUTTA ◽  
M DAS GUPTA ◽  
G R IYENGAR ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Medium Range ◽  
Forecasting System

scholarly journals Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics

ACS Omega ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 2001-2024
Author(s):  
Shuo Ma ◽  
Yingjin Ma ◽  
Baohua Zhang ◽  
Yingqi Tian ◽  
Zhong Jin
Keyword(s):  
Machine Learning ◽  
Computational Time ◽  
Tddft Calculations ◽  
Forecasting System

scholarly journals SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1532 ◽  
Author(s):  
Guido Masiello ◽  
Carmine Serio ◽  
Sara Venafra ◽  
Laurent Poutier ◽  
Frank-M. Göttsche
Keyword(s):  
Radiative Transfer ◽  
Real Time ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Real Time Processing ◽  
In Situ Observations ◽  
Infrared Radiances

Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

scholarly journals Comparative verification in complex topography

2021 ◽  
Author(s):  
Jonas Bhend ◽  
Jean-Christophe Orain ◽  
Vera Schönenberger ◽  
Christoph Spirig ◽  
Lionel Moret ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Absolute Error ◽  
Time Of Day ◽  
Probabilistic Forecasts ◽  
Wide Range ◽  
Direct Model ◽  
Forecasting System ◽  
The Mean ◽  
Update Frequency ◽  
Core Activity

&lt;p&gt;Verification is a core activity in weather forecasting. Insights from verification are used for monitoring, for reporting, to support and motivate development of the forecasting system, and to allow users to maximize forecast value. Due to the broad range of applications for which verification provides valuable input, the range of questions one would like to answer can be very large. Static analyses and summary verification results are often insufficient to cover this broad range. To this end, we developed an interactive verification platform at MeteoSwiss that allows users to inspect verification results from a wide range of angles to find answers to their specific questions.&lt;/p&gt;&lt;p&gt;We present the technical setup to achieve a flexible yet performant interactive platform and two prototype applications: monitoring of direct model output from operational NWP systems and understanding of the capabilities and limitations of our pre-operational postprocessing. We present two innovations that illustrate the user-oriented approach to comparative verification adopted as part of the platform. To facilitate the comparison of a broad range of forecasts issued with varying update frequency, we rely on the concept of time of verification to collocate the most recent available forecasts at the time of day at which the forecasts are used. In addition, we offer a matrix selection to more flexibly select forecast sources and scores for comparison. Doing so, we can for example compare the mean absolute error (MAE) for deterministic forecasts to the MAE and continuous ranked probability scores of probabilistic forecasts to illustrate the benefit of using probabilistic forecasts.&lt;/p&gt;

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

&lt;p&gt;NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.&lt;/p&gt;

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