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Abstract The Tibetan Plateau is regarded as the Earth's Third Pole, which is the source region of several major rivers that impact more 20% the world population. This high‐altitude region is reported to have been undergoing much greater rate of weather changes under global warming, but the existing reanalysis products are inadequate for depicting the state of the atmosphere, particularly with regard to the amount of precipitation and its diurnal cycle. An ensemble Kalman filter (EnKF) data assimilation system based on the limited‐area Weather Research and Forecasting (WRF) model was evaluated for use in developing a regional reanalysis over the Tibetan Plateau and the surrounding regions. A 3‐month prototype reanalysis over the summer months (June−August) of 2015 using WRF‐EnKF at a 30‐km grid spacing to assimilate nonradiance observations from the Global Telecommunications System was developed and evaluated against independent sounding and satellite observations in comparison to the ERA‐Interim and fifth European Centre for Medium‐Range Weather Forecasts Reanalysis (ERA5) global reanalysis. Results showed that both the posterior analysis and the subsequent 6‐ to 12‐hr WRF forecasts of the prototype regional reanalysis compared favorably with independent sounding observations, satellite‐based precipitation versus those from ERA‐Interim and ERA5 during the same period. In particular, the prototype regional reanalysis had clear advantages over the global reanalyses of ERA‐Interim and ERA5 in the analysis accuracy of atmospheric humidity, as well as in the subsequent downscale‐simulated precipitation intensity, spatial distribution, diurnal evolution, and extreme occurrence.
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How Accurate Are Modern Atmospheric Reanalyses for the Data-Sparse Tibetan Plateau Region?
Abstract More than 6000 independent radiosonde observations from three major Tibetan Plateau experiments during the warm seasons (May–August) of 1998, 2008, and 2015–16 are used to assess the quality of four leading modern atmospheric reanalysis products (CFSR/CFSv2, ERA-Interim, JRA-55, and MERRA-2), and the potential impact of satellite data changes on the quality of these reanalyses in the troposphere over this data-sparse region. Although these reanalyses can reproduce reasonably well the overall mean temperature, specific humidity, and horizontal wind profiles against the benchmark independent sounding observations, they have nonnegligible biases that can be potentially bigger than the analysis-simulated mean regional climate trends over this region. The mean biases and mean root-mean-square errors of winds, temperature, and specific humidity from almost all reanalyses are reduced from 1998 to the two later experiment periods. There are also considerable differences in almost all variables across different reanalysis products, though these differences also become smaller during the 2008 and 2015–16 experiments, in particular for the temperature fields. The enormous increase in the volume and quality of satellite observations assimilated into reanalysis systems is likely the primary reason for the improved quality of the reanalyses during the later field experiment periods. Besides differences in the forecast models and data assimilation methodology, the differences in performance between different reanalyses during different field experiment periods may also be contributed by differences in assimilated information (e.g., observation input sources, selected channels for a given satellite sensor, quality-control methods).
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- Award ID(s):
- 1712290
- PAR ID:
- 10118601
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 32
- Issue:
- 21
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- p. 7153-7172
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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