scholarly journals Rapid Update with EnVar Direct Radar Reflectivity Data Assimilation for the NOAA Regional Convection-Allowing NMMB Model over the CONUS: System Description and Initial Experiment Results

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1286
Author(s):  
Yongming Wang ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Grid Point ◽  
Warm Season ◽  
Multiscale Model ◽  
Large Temperature ◽  
In Situ Observation ◽  
The North ◽  
Analysis System ◽  
Reflectivity Data ◽  
The Impact

This study first describes the extended Grid-Point Statistical Interpolation analysis system (GSI)-based ensemble-variational data assimilation (DA) system within the North American Mesoscale Rapid Refresh (NAMRR) system for the Nonhydrostatic Multiscale Model on the B grid (NMMB). Experiments were conducted to examine three critical aspects of data assimilation configuration in this system. Ten retrospective high-impact convective cases during the warm season of 2015–2016 were adopted for testing. A 10-member, 18 h ensemble forecast was launched for each experiment. Specifically, the experiment using horizontal (vertical) localization radii (Lr) of 300 km (0.55-scaled height measured in the nature log of pressure) overall had more skills than that of 500 km (1.1-scaled height) for conventional in-situ observation assimilation. Diagnostics suggest that the higher forecast skills could be attributed to applying smaller Lr in the boundary with large temperature and moisture gradients. For radar DA, the experiment was more skillful with horizontal (vertical) Lr of 15 km (1.1-scaled height) than that of 12 km (0.55-scaled height). Diagnostics suggest that the improved forecasts were achieved by using wider Lr to spread radar observations into unobserved areas more effectively. Slight forecast skill differences between the relaxation inflation factors of 95% and 65% are presented. The impact of varying inflation magnitudes primarily occurred in the upper-level spread.

The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis

2004 ◽  
Vol 43 (5) ◽  
pp. 810-820 ◽  
Author(s):  
L. P. Riishøjgaard ◽  
R. Atlas ◽  
G. D. Emmitt
Keyword(s):  
Data Assimilation ◽  
Weighted Average ◽  
Horizontal Wind ◽  
Atmospheric Flow ◽  
Single Level ◽  
Error Covariance ◽  
Analysis System ◽  
Background Fields ◽  
The Impact ◽  
Meteorological Analysis

Abstract Through the use of observation operators, modern data assimilation systems have the capability to ingest observations of quantities that are not themselves model variables but are mathematically related to those variables. An example of this is the so-called line-of-sight (LOS) winds that a spaceborne Doppler wind lidar (DWL) instrument would provide. The model or data assimilation system ideally would need information about both components of the horizontal wind vectors, whereas the observations in this case would provide only the projection of the wind vector onto a given direction. The estimated or analyzed value is then calculated essentially as a weighted average of the observation itself and the model-simulated value of the observed quantity. To assess the expected impact of a DWL, it is important to examine the extent to which a meteorological analysis can be constrained by the LOS winds. The answer to this question depends on the fundamental character of the atmospheric flow fields that are analyzed, but, just as important, it also depends on the real and assumed error covariance characteristics of these fields. A single-level wind analysis system designed to explore these issues has been built at the NASA Data Assimilation Office. In this system, simulated wind observations can be evaluated in terms of their impact on the analysis quality under various assumptions about their spatial distribution and error characteristics and about the error covariance of the background fields. The basic design of the system and experimental results obtained with it are presented. The experiments were designed to illustrate how such a system may be used in the instrument concept definition phase.

scholarly journals AVALIAÇÃO DO WRF-4DVAR EM UM CASO DE EVENTO SEVERO NO RIO GRANDE DO SUL

2016 ◽  
Vol 38 (2) ◽  
pp. 1077
Author(s):  
Luana Ribeiro Macedo ◽  
João Luiz Martins Basso ◽  
Yoshihiro Yamasaki
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Rio Grande ◽  
Real Data ◽  
Radar Reflectivity ◽  
Rio Grande Do Sul ◽  
Mesoscale System ◽  
Accumulated Rainfall ◽  
Reflectivity Data ◽  
The Impact

The WRF mesoscale system 4DVAR data assimilation technique have been used with the purpose of evaluating the impact of the meteorological data assimilation on the numeric time prognosis over the Rio Grande do Sul state. It has been done utilizing the surface and altitude data. The consistency analysis has been done evaluating the numerical prognosis exploring the differences between the analysis with and without data assimilation. The produced prognosis results have been compared spatially using the TRMM satellite data as well as the Canguçu radar reflectivity data. The accumulated rainfall has been validated and compared spatially with the TRMM data for the time period of 12 hours comprehended between October 29th and 30th of 2014. It was possible to realize that as well as the WRF, the WRFVAR overestimated the rainfall values. The radar reflectivity field without data assimilation for October 30th at 06:00UTC detected most accurately the reflectivity centers over the state. On the other hand this field with data assimilation did not present good skill. The temperature field analyses reveal that the 4DVAR assimilation system contributes, one way or another, presenting a little improvement for some points compared to the real data.

scholarly journals Impact of data assimilation of physical variables on the spring bloom from TOPAZ operational runs in the North Atlantic

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 635-647 ◽  
Author(s):  
A. Samuelsen ◽  
L. Bertino ◽  
C. Hansen
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Spring Bloom ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Sea Surface ◽  
The Arctic ◽  
The North ◽  
The North Atlantic ◽  
The Impact

Abstract. A reanalysis of the North Atlantic spring bloom in 2007 was produced using the real-time analysis from the TOPAZ North Atlantic and Arctic forecasting system. The TOPAZ system uses a hybrid coordinate general circulation ocean model and assimilates physical observations: sea surface anomalies, sea surface temperatures, and sea-ice concentrations using the Ensemble Kalman Filter. This ocean model was coupled to an ecosystem model, NORWECOM (Norwegian Ecological Model System), and the TOPAZ-NORWECOM coupled model was run throughout the spring and summer of 2007. The ecosystem model was run online, restarting from analyzed physical fields (result after data assimilation) every 7 days. Biological variables were not assimilated in the model. The main purpose of the study was to investigate the impact of physical data assimilation on the ecosystem model. This was determined by comparing the results to those from a model without assimilation of physical data. The regions of focus are the North Atlantic and the Arctic Ocean. Assimilation of physical variables does not affect the results from the ecosystem model significantly. The differences between the weekly mean values of chlorophyll are normally within 5–10% during the summer months, and the maximum difference of ~20% occurs in the Arctic, also during summer. Special attention was paid to the nutrient input from the North Atlantic to the Nordic Seas and the impact of ice-assimilation on the ecosystem. The ice-assimilation increased the phytoplankton concentration: because there was less ice in the assimilation run, this increased both the mixing of nutrients during winter and the area where production could occur during summer. The forecast was also compared to remotely sensed chlorophyll, climatological nutrients, and in-situ data. The results show that the model reproduces a realistic annual cycle, but the chlorophyll concentrations tend to be between 0.1 and 1.0 mg chla/m3 too low during winter and spring and 1–2 mg chla/m3 too high during summer. Surface nutrients on the other hand are generally lower than the climatology throughout the year.

Warm Season Lightning Probability Prediction for Canada and the Northern United States

2005 ◽  
Vol 20 (6) ◽  
pp. 971-988 ◽  
Author(s):  
William R. Burrows ◽  
Colin Price ◽  
Laurence J. Wilson
Keyword(s):  
Statistical Models ◽  
Prediction Models ◽  
Grid Point ◽  
Warm Season ◽  
Precipitable Water ◽  
Large Area ◽  
Maximum Probability ◽  
The North ◽  
The Mean ◽  
Structured Regression

Abstract Statistical models valid May–September were developed to predict the probability of lightning in 3-h intervals using observations from the North American Lightning Detection Network and predictors derived from Global Environmental Multiscale (GEM) model output at the Canadian Meteorological Centre. Models were built with pooled data from the years 2000–01 using tree-structured regression. Error reduction by most models was about 0.4–0.7 of initial predictand variance. Many predictors were required to model lightning occurrence for this large area. Highest ranked overall were the Showalter index, mean sea level pressure, and troposphere precipitable water. Three-hour changes of 500-hPa geopotential height, 500–1000-hPa thickness, and MSL pressure were highly ranked in most areas. The 3-h average of most predictors was more important than the mean or maximum (minimum where appropriate). Several predictors outranked CAPE, indicating it must appear with other predictors for successful statistical lightning prediction models. Results presented herein demonstrate that tree-structured regression is a viable method for building statistical models to forecast lightning probability. Real-time forecasts in 3-h intervals to 45–48 h were made in 2003 and 2004. The 2003 verification suggests a hybrid forecast based on a mixture of maximum and mean forecast probabilities in a radius around a grid point and on monthly climatology will improve accuracy. The 2004 verification shows that the hybrid forecasts had positive skill with respect to a reference forecast and performed better than forecasts defined by either the mean or maximum probability at most times. This was achieved even though an increase of resolution and change of convective parameterization scheme were made to the GEM model in May 2004.

scholarly journals Impact of Ocean Observation Systems on Ocean Analysis and Seasonal Forecasts

2007 ◽  
Vol 135 (2) ◽  
pp. 409-429 ◽  
Author(s):  
A. Vidard ◽  
D. L. T. Anderson ◽  
M. Balmaseda
Keyword(s):  
Data Assimilation ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Tropical Atlantic ◽  
Seasonal Forecasts ◽  
The North ◽  
Ocean Data Assimilation ◽  
Observation Systems ◽  
The Impact

Abstract The relative merits of the Tropical Atmosphere–Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TAO/TRITON) and Pilot Research Moored Array in the Tropical Atlantic mooring networks, the Voluntary Observing Ship (VOS) expendable bathythermograph (XBT) network, and the Argo float network are evaluated through their impact on ocean analyses and seasonal forecast skill. An ocean analysis is performed in which all available data are assimilated. In two additional experiments the moorings and the VOS datasets are withheld from the assimilation. To estimate the impact on seasonal forecast skill, the set of ocean analyses is then used to initialize a corresponding set of coupled ocean–atmosphere model forecasts. A further set of experiments is conducted to assess the impact of the more recent Argo array. A key parameter for seasonal forecast initialization is the depth of the thermocline in the tropical Pacific. This depth is quite similar in all of the experiments that involve data assimilation, but withdrawing the TAO data has a bigger effect than withdrawing XBT data, especially in the eastern half of the basin. The forecasts mainly indicate that the TAO/TRITON in situ temperature observations are essential to obtain optimum forecast skill. They are best combined with XBT, however, because this results in better predictions for the west Pacific. Furthermore, the XBTs play an important role in the North Atlantic. The ocean data assimilation performs less well in the tropical Atlantic. This may be partly a result of not having adequate observations of salinity.

scholarly journals Improving Short-Term Storm Predictions by Assimilating both Radar Radial-Wind and Reflectivity Observations

2008 ◽  
Vol 23 (3) ◽  
pp. 373-391 ◽  
Author(s):  
Qingyun Zhao ◽  
John Cook ◽  
Qin Xu ◽  
Paul R. Harasti
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radar Data ◽  
Naval Research ◽  
Short Term ◽  
High Resolution Data ◽  
Reflectivity Data ◽  
The Impact ◽  
Data Assimilation System ◽  
Resolution Data

Abstract A high-resolution data assimilation system is under development at the Naval Research Laboratory (NRL). The objective of this development is to assimilate high-resolution data, especially those from Doppler radars, into the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System to improve the model’s capability and accuracy in short-term (0–6 h) prediction of hazardous weather for nowcasting. A variational approach is used in this system to assimilate the radar observations into the model. The system is upgraded in this study with new capabilities to assimilate not only the radar radial-wind data but also reflectivity data. Two storm cases are selected to test the upgraded system and to study the impact of radar data assimilation on model forecasts. Results from the data assimilation experiments show significant improvements in storm prediction especially when both radar radial-wind and reflectivity observations are assimilated and the analysis incremental fields are adequately constrained by the model’s dynamics and properly adjusted to satisfy the model’s thermodynamical balance.

scholarly journals Response of downstream lakes to Aru glacier collapses on the western Tibetan Plateau

2021 ◽  
Vol 15 (1) ◽  
pp. 199-214
Author(s):  
Yanbin Lei ◽  
Tandong Yao ◽  
Lide Tian ◽  
Yongwei Sheng ◽  
Jingjuan Liao ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Warm Season ◽  
Rapid Expansion ◽  
In Situ Observation ◽  
Glacier Melting ◽  
Lake Morphology ◽  
Western Tibetan Plateau ◽  
The Impact ◽  
Ice Avalanche

Abstract. The lower parts of two glaciers in the Aru range on the western Tibetan Plateau (TP) collapsed on 17 July and 21 September 2016, respectively, causing fatal damage to local people and their livestock. The giant ice avalanches, with a total volume of 150 × 106 m3, had almost melted by September 2019 (about 30 % of the second ice avalanche remained). The impact of these extreme disasters on downstream lakes has not been investigated yet. Based on in situ observation, bathymetry survey and satellite data, we explore the impact of the ice avalanches on the two downstream lakes (i.e., Aru Co and Memar Co) in terms of lake morphology, water level and water temperature in the subsequent 4 years (2016–2019). After the first glacier collapse, the ice avalanche slid into Aru Co along with a large amount of debris, which generated great impact waves in Aru Co and significantly modified the lake's shoreline and underwater topography. An ice volume of at least 7.1 × 106 m3 was discharged into Aru Co, spread over the lake surface and considerably lowered its surface temperature by 2–4 ∘C in the first 2 weeks after the first glacier collapse. Due to the large amount of meltwater input, Memar Co exhibited more rapid expansion after the glacier collapses (2016–2019) than before (2003–2014), in particular during the warm season. The melting of ice avalanches was found to contribute to about 23 % of the increase in lake storage between 2016 and 2019. Our results indicate that the Aru glacier collapses had both short-term and long-term impacts on the downstream lakes and provide a baseline in understanding the future lake response to glacier melting on the TP under a warming climate.

scholarly journals Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System

2016 ◽  
Vol 17 (7) ◽  
pp. 1951-1972 ◽  
Author(s):  
Sujay V. Kumar ◽  
Benjamin F. Zaitchik ◽  
Christa D. Peters-Lidard ◽  
Matthew Rodell ◽  
Rolf Reichle ◽  
...  
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Eastern United States ◽  
Terrestrial Water Storage ◽  
The North ◽  
Terrestrial Water ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System

Abstract The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

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