scholarly journals Diagnosing atmospheric motion vector observation errors for an operational high-resolution data assimilation system

2016 ◽  
Vol 143 (702) ◽  
pp. 333-341 ◽  
Author(s):  
M. Cordoba ◽  
S. L. Dance ◽  
G. A. Kelly ◽  
N. K. Nichols ◽  
J. A. Waller
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Motion Vector ◽  
High Resolution Data ◽  
Vector Observation ◽  
Atmospheric Motion ◽  
Data Assimilation System ◽  
Resolution Data ◽  
Assimilation System

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 Recent Progress of the NHM-4DVAR towards a Super-High Resolution Data Assimilation

SOLA ◽  
2014 ◽  
Vol 10 (0) ◽  
pp. 145-149 ◽  
Author(s):  
Takuya Kawabata ◽  
Kosuke Ito ◽  
Kazuo Saito
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Recent Progress ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals CTDAS-Lagrange v1.0: A high-resolution data assimilation system for regional carbon dioxide observations

2017 ◽  
Author(s):  
Wei He ◽  
Ivar R. van der Velde ◽  
Arlyn E. Andrews ◽  
Colm Sweeney ◽  
John Miller ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Transport Model ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
Sensitivity Tests ◽  
Lagrange System ◽  
Data Assimilation System ◽  
Flux Adjustment ◽  
Assimilation System

Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named as CTDAS‑Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft Programmable Flask Packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties of the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical data set derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model-data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing NEE than the multiplicative flux adjustment method, and that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral boundary conditions and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from −0.92 to −1.26 PgC/yr. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, −0.91 ± 1.10 PgC/yr) and CarbonTracker Europe (version CTE2016, −0.91 ± 0.31 PgC/yr). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.

scholarly journals High-Resolution (2.5 km) Ensemble Precipitation Analysis across Canada

2020 ◽  
Vol 21 (9) ◽  
pp. 2023-2039
Author(s):  
Dikra Khedhaouiria ◽  
Stéphane Bélair ◽  
Vincent Fortin ◽  
Guy Roy ◽  
Franck Lespinas
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Background Field ◽  
Spatiotemporal Resolution ◽  
Stochastic Perturbations ◽  
Multiple Observations ◽  
Uncertainty Estimates ◽  
Precipitation Analysis ◽  
Data Assimilation System ◽  
Assimilation System

AbstractConsistent and continuous fields provided by precipitation analyses are valuable for hydrometeorological applications and land data assimilation modeling, among others. Providing uncertainty estimates is a logical step in the analysis development, and a consistent approach to reach this objective is the production of an ensemble analysis. In the present study, a 6-h High-Resolution Ensemble Precipitation Analysis (HREPA) was developed for the domain covering Canada and the northern part of the contiguous United States. The data assimilation system is the same as the Canadian Precipitation Analysis (CaPA) and is based on optimal interpolation (OI). Precipitation from the Canadian national 2.5-km atmospheric prediction system constitutes the background field of the analysis, while at-site records and radar quantitative precipitation estimates (QPE) compose the observation datasets. By using stochastic perturbations, multiple observations and background field random realizations were generated to subsequently feed the data assimilation system and provide 24 HREPA members plus one control run. Based on one summer and one winter experiment, HREPA capabilities in terms of bias and skill were verified against at-site observations for different climatic regions. The results indicated HREPA’s reliability and skill for almost all types of precipitation events in winter, and for precipitation of medium intensity in summer. For both seasons, HREPA displayed resolution and sharpness. The overall good performance of HREPA and the lack of ensemble precipitation analysis (PA) at such spatiotemporal resolution in the literature motivate further investigations on transitional seasons and more advanced perturbation approaches.

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