Data Assimilation Of Observations Research Articles

Factors influencing the skill of synthesized satellite wind products in the tropical Pacific

Given the importance of tropical Pacific winds to global climate, it is interesting to examine differences in the mean and trend among various wind products, and their implications for ocean circulation. Past analysis has revealed that despite the assimilation of observational data, there remain large differences among reanalysis products. Thus, here we examine if satellite‐based synthesis products may provide more consistent estimate than reanalysis. Reanalysis product winds are, however, typically used as a background constraint in constructing the synthesis products to fill spatiotemporal gaps and to deal with satellite wind direction ambiguity. Our study identified two important factors that influence both the mean and trends from synthesized wind products. First, the choice of background wind product in synthesized satellite wind products affects the mean and long‐term trends, which has implications for simulations of ocean circulation, sea level, and presumably SST. Second, we identify a clear need for developing a better understanding of, and correcting differences between in situ observations of absolute winds with the satellite‐derived relative winds prior to synthesizing. This correction requires careful analysis of satellite surface winds with existing colocated in situ measurements of surface winds and currents, and will benefit from near‐surface current observations of the proposed Tropical Pacific Observing System. These results also illustrate the difficulty in independently evaluating the synthesis wind products because the in situ data have been utilized at numerous steps during their development. Addressing these identified issues effectively, will require enhanced collaborations among the wind observation (both satellite and in situ), reanalysis, and synthesis communities.

Retrieval of Cloud Water Variables by 1D-Var Algorithm

Data assimilation of satellite observations in cloud and rain regions is a worldwide problem. In the paper, we designed the background field and their error covariance of three cloud water variables (cloud cover, cloud liquid water and cloud ice water) at first. The experiment results demonstrate that 1D-Var (One-dimensional variational data assimilation) can retrieve the three variables efficiently. The cloud liquid water and cloud ice water can be obviously improved by 1D-Var. However, the retrieved effect of low cloud cover is still to be improved. It has the positive potential to assimilate the satellite observations in cloud and rain regions.

Variational Approach to the Study of Processes of Geophysical Hydrothermodynamics with Assimilation of Detailed Observation Data

Variational Approach to the Study of Processes of Geophysical Hydrothermodynamics with Assimilation of Detailed Observation Data

Satellite radiance data assimilation for rainfall prediction in Java Region

This study examined the influence of satellite radiance data assimilation for predicting two days of heavy rainfall in the Java region. The first case occurred from 22 to 23 on January 2015 while the second case occurred from 1 to 2 on February 2015. The analysis examined before and after data assimilation in the two cases study. The Global Forecast System (GFS) data were used as initial condition which was assimilated with several data such as surface observation data, radiance data from AMSUA sensor, radiance data from HIRS sensor, and radiance data from MHS sensor. Weather Research and Forecasting Data Assimilation (WRFDA) is a tool which is used in this study for assimilating process with Three Dimensional Variation (3D-Var) method. The Quantitative Precipitation Forecast (QPF) skill was used to evaluate influence data assimilation for rainfall prediction. The result of the study obtained different rainfall prediction with different data assimilation. In general, the surface observation data assimilation has lower QPF skill than the satellite radiance data assimilation. Even thought radiance data assimilation has slightly contribution on rainfall prediction, but it gave better accuracy on rainfall prediction for two heavy rainfall cases.

Using 3D-Var Data Assimilation for Improving the Accuracy of Initial Condition of Weather Research and Forecasting (WRF) Model in Java Region (Case Study : 23 January 2015)

Weather Research and Forecasting (WRF) is a numerical weather prediction model developed by various parties due to its open source, but the WRF has the disadvantage of low accuracy in weather prediction. One reason of low accuracy of model is inaccuracy initial condition model to the actual atmospheric conditions. Techniques to improve the initial condition model is the observation data assimilation. In this study, we used three-dimensional variational (3D-Var) to perform data assimilation of some observation data. Observational data used in data assimilation are observation data from basic stations, non-basic stations, radiosonde data, and The Binary Universal Form for the Representation of meteorological data (BUFR) data from the National Centers for Environmental Prediction (NCEP) , and aggregate observation data from all stations. The aim of this study compares the effect of data assimilation with different data observation on January 23, 2015 at 00.00 UTC for Java island region. The results showed that changes root mean square error (RMSE) of surface temperature from 2° C to 1.7° C - 2.4° C, dew point from 2.1o C to 1.9o C - 1.4o C, relative humidity from 16.1% to 3.5% - 14.5% after the data assimilation.

Correction of inertial oscillations by assimilation of HF radar data in a model of the Ligurian Sea

This article aims at analyzing if high-frequency radar observations of surface currents allow to improve model forecasts in the Ligurian Sea, where inertial oscillations are a dominant feature. An ensemble of ROMS models covering the Ligurian Sea, and nested in the Mediterranean Forecasting System, is coupled with two WERA high-frequency radars. A sensitivity study allows to determine optimal parameters for the ensemble filter. By assimilating observations in a single point, the obtained correction shows that the forecast error covariance matrix represents the inertial oscillations, as well as large- and meso-scale processes. Furthermore, it is shown that the velocity observations can correct the phase and amplitude of the inertial oscillations. Observations are shown to have a strong effect during approximately half a day, which confirms the importance of using a high temporal observation frequency. In general, data assimilation of HF radar observations leads to a skill score of about 30% for the forecasts of surface velocity.

Diagnosing Observation Error Correlations for Doppler Radar Radial Winds in the Met Office UKV Model Using Observation-Minus-Background and Observation-Minus-Analysis Statistics

AbstractWith the development of convection-permitting numerical weather prediction the efficient use of high-resolution observations in data assimilation is becoming increasingly important. The operational assimilation of these observations, such as Doppler radar radial winds (DRWs), is now common, although to avoid violating the assumption of uncorrelated observation errors the observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of the full, potentially correlated, error statistics. In this work, observation error statistics are calculated for the DRWs that are assimilated into the Met Office high-resolution U.K. model (UKV) using a diagnostic that makes use of statistical averages of observation-minus-background and observation-minus-analysis residuals. This is the first in-depth study using the diagnostic to estimate both horizontal and along-beam observation error statistics. The new results obtained show that the DRW error standard deviations are similar to those used operationally and increase as the observation height increases. Surprisingly, the estimated observation error correlation length scales are longer than the operational thinning distance. They are dependent both on the height of the observation and on the distance of the observation away from the radar. Further tests show that the long correlations cannot be attributed to the background error covariance matrix used in the assimilation, although they are, in part, a result of using superobservations and a simplified observation operator. The inclusion of correlated error statistics in the assimilation allows less thinning of the data and hence better use of the high-resolution observations.

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Abstract. In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a time-invariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation. This was especially true for snow-rich years. These findings suggest that with increasing elevation and the correspondingly increased contribution of snowmelt to runoff, the accurate estimation of snow water equivalent (SWE) and snowmelt rates has gained importance.

Long-term trend of cold air mass amount below a designated potential temperature in Northern and Southern Hemispheric winters using reanalysis data sets

This study shows that the total hemispheric cold air mass (CAM) amount below a designated potential temperature of 280 K serves as a good indicator of long-term climate change trends in the polar region. We conduct quantitative analyses of the warming trends of Northern Hemisphere (NH) and Southern Hemisphere (SH) winters using five reanalysis datasets (CFSR, ERA-interim, JRA-55, JRA-25, and NCEP-NCAR) and JRA-55-related datasets (JRA-55C and JRA-55AMIP). Total hemispheric CAM amount for the NH winter shows a significant decreasing trend for most of the reanalyses at a rate of approximately -1.37 to -0.77% decade-1 from 1959 to 2012 and at a rate of approximately -1.57 to -0.84% decade-1 from 1980 to 2012. The CAM amount trends show very different geographic patterns during the two periods, which suggests that geographical patterns of the trends estimated for these periods are largely controlled by internal low-frequency dynamics. The CAM flux across 45°N, which serves as an indicator of hemispheric scale cold air outbreaks, has so large interannual variability that its trend becomes insignificant. In the SH winter, trends of total hemispheric CAM amount and its equatorward flux across 50°S are not consistent among the new reanalyses for 1980-2012. Although total hemispheric CAM amount decreases for 1959-2012, sparse observations and changes in systematic bias, due to the implementation of assimilation of satellite observation data in 1979, compromise the reliability of the trends. Improvements to the reanalyses are desirable as a means of monitoring historical changes in CAM for the SH winter.

Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

The numerical forecast of particulate matter concentrations in general, and PM10 in particular is a theme of high socio-economic relevance. The aim of this study was to investigate the impact of ground and satellite data assimilation of PM10 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) numerical air quality model for Romanian territory. This is the first initiative of the kind for this domain of interest. Assimilation of satellite information – e.g. AOT's in air quality models is of interest due to the vast spatial coverage of the observations. Support Vector Regression (SVR) techniques are used to estimate the PM content from heterogeneous data sources, including EO products (Aerosol Optical Thickness), ground measurements and numerical model data (temperature, humidity, wind, etc.). In this study we describe the modeling framework employed and present the evaluation of the impact from the data assimilation of PM10 observations on the forecast of the WRF-CHEM model. Integrations of the WRF-CHEM model in data assimilation enabled/disabled configurations allowed the evaluation of satellite and ground data assimilation impact on the PM10 forecast performance for the Romanian territory. The model integration and evaluation were performed for two months, one in winter conditions (January 2013) and one in summer conditions (June 2013).

Numerical modeling of ocean hydrodynamics with variational assimilation of observational data

Models and methods of the numerical modeling of ocean hydrodynamics dating back to the pioneering works of A.S. Sarkisyan are considered, with emphasis on the formulation of problems and algorithms of mathematical modeling and the four-dimensional variational assimilation of observational data. An algorithm is proposed for studying the sensitivity of the optimal solution to observational data errors in a seasurface temperature assimilation problem in order to retrieve heat fluxes on the surface. An example of a solution of the optimal problem of the World Ocean hydrodynamics with the assimilation of climatic temperature and salinity observations is offered.

Big data analytics: six techniques

Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics is related to the entire methodology rather than the individual specific analysis. In this paper, six techniques concerning big data analytics are proposed, which include: (1) Ensemble analysis related to a large volume of data, (2) Association analysis related to unknown data sampling, (3) High-dimensional analysis related to a variety of data, (4) Deep analysis related to the veracity of data, (5) Precision analysis related to the veracity of data, and (6) Divide-and-conquer analysis related to the velocity of data. The essential of big data analytics is the structural analysis of big data in an optimal criterion of physics, computation, and human cognition. Fundamentally, two theoretical challenges, ie the violation of independent and identical distribution, and the extension of general set-theory, are posed. In particular, we have illustrated three kinds of association in geographical big data, ie geometrical associations in space and time, spatiotemporal correlations in statistics, and space-time relations in semantics. Furthermore, we have illustrated three kinds of spatiotemporal data analysis, ie measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations, from which spatiotemporal big data analysis may be largely derived.

Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia–Biscay–Ireland (IBI) waters

The present study is aimed at intercomparing the ocean physical daily forecast and 10-year (2002–2012) reanalysis products provided by the Iberia–Biscay–Ireland Monitoring and Forecasting Center (IBI-MFC), in the framework of the Copernicus Marine Environment Monitoring Service (CMEMS), over an overlapping 9-month period (April–December 2011). These two products differ in their spatial resolution and in the use of an observational data assimilation scheme in the reanalysis. Both modeled solutions are compared at regional and local scale against several observational data sources. At regional scale, the forecast and reanalysis show realistic patterns in the area of study. However, at finer scales the results highlight better performances of the 1/36° forecast in coastal areas and the 1/12° reanalysis over open waters. The comparison emphasizes the possible benefits of the data assimilation scheme in areas away from the coastline, but also its limitations in complex coastal regions. Spatial resolution seems to play a key role in such areas, especially around the Iberian Peninsula, where the higher resolution forecast brings in general better results than the coarser resolution reanalysis. The study suggests that the observational data assimilation represents a crucial step towards improving the performance of regional modeled solutions, as long as the spatial resolution is kept at fine-enough meshes in order to prevent higher uncertainties in coastal and shelf areas.

Wind waves in the coastal zone of the southern crimea: Assessment of simulation quality based on in situ measurements

The study verifies the Black Sea wave model using field data obtained from the Katsiveli research platform. The WAM and mesoscale MM5 and WRF atmospheric models, which are used to calculate the wind field for the wave model, were recently adjusted to the Black Sea region at the Marine Hydrophysical Institute. The results of the work are presented as characteristics of the simulation quality used in world practice in other regions. The scatter index for a significant wave height is 70% in summer and 50% in winter. The values of the scatter index of wave parameters and wind speed appear to be at the same level as in semi-enclosed seas on the northern side of the Mediterranean Sea. It is shown that atmospheric simulation correctly reproduces the interaction between synoptic processes and the mountain range extending alongshore. Error sources in wave simulation are discussed. The most significant drawback is the possibility of mesoscale instability in the atmospheric model without assimilation of observation data within the computational domain.

Establishing the Advanced Disaster Reduction Management System by Fusion of Real-Time Disaster Simulation and Big Data Assimilation

A project titled “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation,” was launched as Core Research for Evolutional Science and Technology (CREST) by the Japan Science and Technology Agency (JST). Intended to save as many lives as possible in future national crises involving earthquake and tsunami disasters, the project works on a disaster mitigation system of the big data era, based on cooperation of large-scale, high-resolution, real-time numerical simulations and assimilation of real-time observation data. The world’s most advanced specialists in disaster simulation, disaster management, mathematical science, and information science work together to create the world’s first analysis platform for real-time simulation and big data that effectively processes, analyzes, and assimilates data obtained through various observations. Based on quantitative data, the platform designs proactive measures and supports disaster operations immediately after disaster occurrence. The project was launched in 2014 and is working on the following issues at present.Sophistication and fusion of simulations and damage prediction models using observational big data: Development of a real-time simulation core system that predicts the time evolution of disaster effect by assimilating of location information, fire information, and building collapse information which are obtained from mobile terminals, satellite images, aerial images, and other new observation data in addition to sensing data obtained by the undersea high-density seismic observation network.Latent structure analysis and major disaster scenario creation based on a huge amount of simulation results: Development of an analysis and extraction method for the latent structure of a huge amount of disaster scenarios generated by simulation, and creation of severe scenarios with minimum “unexpectedness” by controlling disaster scenario explosion (an explosive increase in the number of predicted scenarios).Establishment of an earthquake and tsunami disaster mitigation big data analysis platform: Development of an earthquake and tsunami disaster mitigation big data analysis platform that realizes analyses of a huge number of disaster scenarios and increases in speed of data assimilation, and clarifies the requirements for operation of the platform as a disaster mitigation system.The project was launched in 2014 as a 5-year project. It consists of element technology development and system fusion, feasibility study as a next-generation disaster mitigation system (validation with/without introduction of the developed real-time simulation and big data analysis platform) in the affected areas of the Great East Japan Earthquake, and test operations in affected areas of the Tokyo metropolitan earthquake and the Nankai Trough earthquake.

Numerical Weather Prediction for Himalayan Complex Terrain: Prospects of Variational Data Assimilation

<p>As part of the ongoing research activities at National Atmospheric Resource and Environmental Research Laboratory (NARERL) to realize high spatial and temporal resolution weather forecasts for Nepal, the Weather Research and Forecasting (WRF) modeling system performance with the National Center for Environmental Protection (NCEP) and National Center for Medium Range Weather Forecast (NCMRWF) initialization global meteorological data sets and the effect of surface observation data assimilation have been examined. The study shows that WRF modeling system reasonably well predicts the diurnal variation of upcoming weather events with both the data sets. The observation data assimilation from entire weather station distributed over the country may lead to the significant improvement in the accuracy and reliability of extended period of forecast. However, upper air observation data assimilation would be necessary to achieve desired precision and reliability of extended weather forecast.</p><p>Journal of Nepal Physical Society Vol.3(1) 2015: 67-72</p>

Data Assimilation of Himawari-8 Aerosol Observations: Asian Dust Forecast in June 2015

Data Assimilation of Himawari-8 Aerosol Observations: Asian Dust Forecast in June 2015

The study and numerical solution of the problem of heat and salinity transfer assuming ‘liquid’ boundaries

AbstractThe problem of simulation of physical processes in water areas with ‘liquid’ boundaries is well known in geophysical hydrodynamics. In this paper we consider a problem of heat and salinity propagation as an example of approaches solving such problems and consisting in application of methods of variational assimilation of observation data. Using well known techniques of study and solution of inverse problems and optimal control problems, we propose an iterative solution algorithm, obtain conditions for existence of the solution, for unique and dense solvability of the problem, and for convergence of the iterative algorithm, The paper also presents results of numerical implementation of this algorithm in application to the water area of the Baltic sea.

The Cause of Internal Outflow Surges in a High-Resolution Simulation of the 8 May 2003 Oklahoma City Tornadic Supercell

Abstract A high-resolution simulation of the 8 May 2003 Oklahoma tornadic supercell is analyzed to determine the origin of internal outflow surges within the low-level cold pool. The analyzed simulation has 50-m horizontal grid spacing and is quadruply nested within larger, lower-resolution domains that were initialized via three-dimensional variational data assimilation (3DVAR) of radar and other observations. The high-resolution simulation produces two tornadoes that track in close proximity to the observed tornado on 8 May 2003. The authors’ previous study determined that an internal outflow surge instigated tornadogenesis for the first tornado in this simulation but the cause of this internal outflow surge was unclear. In this study, the vertical momentum equation is analyzed along backward trajectories that are initialized within the tornado-triggering internal outflow surge. The analysis reveals that the internal outflow surge is forced by the dynamic part of the vertical pressure gradient. Further examination reveals that the dynamic forcing is the result of a high pressure perturbation in an area of stagnating flow on the west and northwest sides of the low-level (below ~3 km AGL) mesocyclone. This region of high perturbation pressure is unsteady and forces several other warm internal outflow surges on the west side of the tornado. Cold internal outflow surges also occur later in the simulation and are shown to be buoyantly forced by evaporation and water loading in heavy precipitation.

Numerical Prediction of Extreme Precipitation over a Truly Complex Terrain of Nepal Himalaya

The predictability of extreme precipitation over truly complex terrain of Nepal Himalaya using Weather Research and Forecasting (WRF) Modeling System has been examined with the case study of record high precipitation (357 mm in 24-hours) over the Pokhara area on the day of 18 August 2001 initialized with NCEP Reanalysis Global Data sets of 1° x 1° resolution without appreciating observation data assimilation. The model resolved the essential features of the precipitation pattern over the area although the model underestimated the reported amount of precipitation as much as 87 mm. The difference may be attributed to the highly localized intense precipitation due to local effects. Significant improvement in the accuracy of prediction can be expected with detail regional meteorological data assimilation.Journal of Institute of Science and Technology, 2015, 20(1): 15-19