Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system's time evolution. Rather than solving the problem from scratch each time new observations become available, one uses the model to ``forecast'' the current state, using a prior state estimate (which incorporates information from past data) as the initial condition, then uses current data to correct the prior forecast to a current state estimate. This Bayesian approach is most effective when the uncertainty in both the observations and in the state estimate, as it evolves over time, are accurately quantified. In this article, we describe a practical method for data assimilation in large, spatiotemporally chaotic systems. The method is a type of ``ensemble Kalman filter'', in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states. We discuss both the mathematical basis of this approach and its implementation; our primary emphasis is on ease of use and computational speed rather than improving accuracy over previously published approaches to ensemble Kalman filtering. We include some numerical results demonstrating the efficiency and accuracy of our implementation for assimilating real atmospheric data with the global forecast model used by the U.S. National Weather Service.

Numerical Weather Prediction

Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter

Assessing the impacts of land use and land cover change (LUCC) on regional climate is essential for understanding land–atmosphere interactions and for designing climate adaptation and mitigation strategies. Using the weather research and forecasting (WRF) model, we examined how different land use and land cover patterns affect regional climate in the agro-pastoral transitional zone of North China, whose environmental and socioeconomic conditions are sensitive to climate change. We parameterized WRF using land use and land cover maps corresponding to 2001 and 2010 conditions, which differ in the representation of four land surface biophysical parameters: vegetation fraction, leaf area index (LAI), albedo, and emissivity. From 2001 to 2010, vegetation fraction and LAI increased in summer, emissivity increased and albedo decreased in winter. Our WRF simulations show that differences in land use and land cover patterns led to widespread reduction in summer temperature with local cooling on the order of 1 °C, and extensive increase in winter temperature with local warming exceeding 0.8 °C. By contrast, simulations using the default landscape representation, provided by WRF itself, show only minor and random changes in temperature. Model evaluation further reveals that our simulations with appropriate land surface properties improve the performance of the WRF model. Our findings demonstrate that LUCC in Northern China has altered the regional climate over the past decade. The magnitude and spatial patterns of temperature changes quantified by our simulations provide useful information for understanding the impacts of LUCC on climate and for developing mitigation and adaptation strategies in arid and semiarid regions.

https://iopscience.iop.org/article/10.1088/1748-9326/10/12/124025/pdf

Impacts of land use and land cover change on regional climate: A case study in the agro-pastoral transitional zone of China

Ensemble forecasting has been operational at NCEP (formerly the National Meteorological Center) since December 1992. In March 1994, more ensemble forecast members were added. In the new configuration, 17 forecasts with the NCEP global model are run every day, out to 16-day lead time. Beyond the 3 control forecasts (a T126 and a T62 resolution control at 0000 UTC and a T126 control at 1200 UTC), 14 perturbed forecasts are made at the reduced T62 resolution. Global products from the ensemble forecasts are available from NCEP via anonymous FTP. The initial perturbation vectors are derived from seven independent breeding cycles, where the fast-growing nonlinear perturbations grow freely, apart from the periodic rescaling that keeps their magnitude compatible with the estimated uncertainty within the control analysis. The breeding process is an integral part of the extended-range forecasts, and the generation of the initial perturbations for the ensemble is done at no computational cost beyond that of...

background

A Synoptic Evaluation of the NCEP Ensemble

Lake Kinneret is a 168-km2 lake located in northern Israel. It provides about 50% of the drinking water consumed in this arid country. To manage correctly this vital water resource, it is essential to understand the various hydrometeorological processes that affect its water budget and, in particular, its evaporation. The complexity of the terrain in this region (varying from ≈2800 m to ≈−410 m within a short distance), combined with different types of soil and ground covers surrounding the lake, results in complicated microscale and mesoscale atmospheric motions, including sea, lake, and land breezes, as well as anabatic and katabatic winds. The Regional Atmospheric Modeling System (RAMS), a state-of-the-art nonhydrostatic model with two-way interactive multigrid nesting and four-dimensional data assimilation capabilities, was used, together with observations collected near the western and eastern shores of the lake, to study these processes. It was configured with two nested grids centered in t...

https://journals.ametsoc.org/doi/pdf/10.1175/1525-7541%282000%29001%3C0095%3ASOTSHP%3E2.0.CO%3B2

Numerical Weather Prediction

Citations

Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter

Impacts of land use and land cover change on regional climate: A case study in the agro-pastoral transitional zone of China

A Synoptic Evaluation of the NCEP Ensemble

Simulations of the summer hydrometeorological processes of Lake Kinneret

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