Multiconstituent data assimilation with WRF‐Chem/DART: Potential for adjusting anthropogenic emissions and improving air quality forecasts over eastern China
C Ma, T Wang, AP Mizzi, JL Anderson… - Journal of …, 2019 - Wiley Online Library
Abstract We use the Weather Research and Forecasting Model with the chemistry/Data
Assimilation Research Testbed (WRF‐Chem/DART) chemical weather forecasting/data …
Assimilation Research Testbed (WRF‐Chem/DART) chemical weather forecasting/data …
Development of the real‐time 30‐s‐update big data assimilation system for convective rainfall prediction with a phased array weather radar: Description and …
We present the first ever real‐time numerical weather prediction system with 30‐s update
cycles at a 500‐m grid spacing for the prediction of convective precipitation in the …
cycles at a 500‐m grid spacing for the prediction of convective precipitation in the …
On the properties of ensemble forecast sensitivity to observations
Evaluating impacts of observations on the skill of numerical weather prediction (NWP) is
important. The Ensemble Forecast Sensitivity to Observation (EFSO) provides an efficient …
important. The Ensemble Forecast Sensitivity to Observation (EFSO) provides an efficient …
Predictability of record-breaking rainfall in Japan in July 2018: Ensemble forecast experiments with the near-real-time global atmospheric data assimilation system …
Abstract 1 This paper is the first publication presenting the predictability of the record-
breaking 2 rainfall in Japan in July 2018 (RJJ18), the severest flood-related disaster since …
breaking 2 rainfall in Japan in July 2018 (RJJ18), the severest flood-related disaster since …
Feasibility of formulating ecosystem biogeochemical models from established physical rules
To improve the predictive capability of ecosystem biogeochemical models (EBMs), we
discuss the feasibility of formulating biogeochemical processes using physical rules that …
discuss the feasibility of formulating biogeochemical processes using physical rules that …
Adaptive tuning of uncertain parameters in a numerical weather prediction model based upon data assimilation
G Zängl - Quarterly Journal of the Royal Meteorological Society, 2023 - Wiley Online Library
In numerical weather prediction models, near‐surface quantities like 10‐m wind speed
(FF10M) or 2‐m temperature (T2M) tend to exhibit significantly larger forecast errors than the …
(FF10M) or 2‐m temperature (T2M) tend to exhibit significantly larger forecast errors than the …
Combined state-parameter estimation with the LETKF for convective-scale weather forecasting
Y Ruckstuhl, T Janjić - Monthly Weather Review, 2020 - journals.ametsoc.org
We investigate the feasibility of addressing model error by perturbing and estimating
uncertain static model parameters using the localized ensemble transform Kalman filter. In …
uncertain static model parameters using the localized ensemble transform Kalman filter. In …
[HTML][HTML] Global precipitation forecasts by merging extrapolation-based nowcast and numerical weather prediction with locally optimized weights
Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather
Prediction with Locally Optimized Weights in: Weather and Forecasting Volume 34 Issue 3 …
Prediction with Locally Optimized Weights in: Weather and Forecasting Volume 34 Issue 3 …
[HTML][HTML] Improvements in supercooled liquid water simulations of low-level mixed-phase clouds over the Southern Ocean using a single-column model
T Seiki, W Roh - Journal of the Atmospheric Sciences, 2020 - journals.ametsoc.org
Improvements in Supercooled Liquid Water Simulations of Low-Level Mixed-Phase Clouds over
the Southern Ocean Using a Single-Column Model in: Journal of the Atmospheric Sciences …
the Southern Ocean Using a Single-Column Model in: Journal of the Atmospheric Sciences …
Data assimilation for climate research: model parameter estimation of large‐scale condensation scheme
This study proposes using data assimilation (DA) for climate research as a tool for optimizing
model parameters objectively. Mitigating radiation bias is very important for climate change …
model parameters objectively. Mitigating radiation bias is very important for climate change …