Precipitation Forecast Characteristics of Radar Data Assimilation Based on Precipitation Types

Radar observation data with high temporal and spatial resolution are used in the data assimilation experiment to improve precipitation forecast of a numerical model. The numerical model considered in this study is Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated radar equivalent reflectivity factor using higher resolution WRF and compared with radar observations in South Korea. To compare the precipitation forecast characteristics of three-dimensional variational (3D-Var) assimilation of radar data, four experiments are performed based on different precipitation types. Comparisons of the 24-h accumulated rainfall with Automatic Weather Station (AWS) data, Contoured Frequency by Altitude Diagram (CFAD), Time Height Cross Sections (THCS), and vertical hydrometeor profiles are used to evaluate and compare the accuracy. The model simulations are performed with and with-out 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The radar data assimilation experiment improved the location of precipitation area and rainfall intensity compared to the control run. Especially, for the two convective cases, simulating mesoscale convective system was greatly improved.

Observing System Simulation Experiment (OSSE) of future ALTIUS ozone profiles

<p>ALTIUS (Atmospheric Limb Tracker for the Investigation of the Upcoming Stratosphere) is a satellite mission dedicated to continue Earth limb measurements for atmospheric sciences (Fussen et al., JQSRT, 2019). It is an element of the ESA Earth Watch programme and is expected to be launched in 2024 on a low earth polar orbit. The instrument is based on three spectral imagers that will measure in UV-vis-NIR wavelength range and will operate in different viewing geometry: limb scattering and occultation of the sun, the moon, the planets and the stars. ALTIUS will retrieve vertical profiles of ozone, nitrogen dioxide, aerosol extinction, among others.</p><p>In this study, we present an Observing System Simulation Experiment (OSSE) of ALTIUS ozone profiles that we have compared with the existing observations from Aura Microwave Limb Sounder (MLS). For this purpose, we have created a stratospheric ozone reference dataset between June and September 2008 based on the assimilation of MLS data with the Belgian Assimilation System for Chemical Observations (BASCOE). During the MLS assimilation experiment, the ozone state is saved in the space of ALTIUS previously determined with the ALTIUS orbit simulator, then perturbed according to the ALTIUS error budget, which creates ALTIUS synthetic observations. The assimilation of these ALTIUS ozone profiles agrees well with those of MLS. The assimilation of the different modes of ALTIUS reveals that all modes are necessary to constrain ozone during the polar night: solar and stellar occultations are the most constraining during the June-August period while limb scattering profiles are the most constraining from September onward.</p><div> </div>

Retrospective analysis of 2015–2017 wintertime PM_2.5 in China: response to emission regulations and the role of meteorology

To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m−3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m−3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within ±5 µg m−3. The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m−3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m−3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m−3, while emission control measures caused a decrease of 8 µg m−3, and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.

Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

Data assimilation with an improved particle filter and its application in the TRIGRS landslide model

Particle filters have become a popular algorithm in data assimilation for their ability to handle nonlinear or non-Gaussian state-space models, but they have significant disadvantages. In this work, an improved particle filter algorithm is proposed. To overcome the particle degeneration and improve particles' efficiency, the processes of particle resampling and particle transfer are updated. In this improved algorithm, particle propagation and the resampling method are ameliorated. The new particle filter is applied to the Lorenz-63 model, and its feasibility and effectiveness are verified using only 20 particles. The root-mean-square difference (RMSD) of estimations converges to stable when there are more than 20 particles. Finally, we choose a peristaltic landslide model and carry out an assimilation experiment of 20 days. Results show that the estimations of states can effectively correct the running offset of the model and the RMSD is convergent after 3 days of assimilation.

Retrospective analysis of 2015–2017 winter-time PM_2.5 in China: response to emission regulations and the role of meteorology

To better characterize the anthropogenic emission relevant aerosol species, the GSI-WRF/Chem data assimilation system was updated from the GOCART aerosol scheme to MOSAIC-4BIN scheme. Three year (2015–2017) winter-time (January) surface PM2.5 observations from 1600+ sites were assimilated hourly using the updated 3DVAR system in the assimilation experiment CONC_DA. Parallel control experiment that did not employ DA (NO_DA) was also performed. Both experiments were verified against the surface PM2.5 observations, MODIS 550-nm AOD and also 550-nm AOD at 9 AERONET sites. In the NO_DA experiment using 2010_MEIC emissions, modeled PM2.5 are severely overestimated in Sichuan Basin (SB), Central China (CC), YRD (Yangzi River Delta), and PRD (Pearl River Delta) which indicated the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations in Northeastern China (NEC) and Xin Jiang (XJ) were also observed. The assimilation experiments significantly reduced the high biases of surface PM2.5 in SB, CC, YRD, and PRD, and also low biases in NEC. However the improvement of the low biases in XJ is relatively small due to the large difference between the observations and the model background in the DA process, likely indicating that the emissions in the model are seriously underestimated in this region. Assimilating surface PM2.5 also significantly changed the column AOD and resulted in closer agreement with MODIS data and observations at AERONET sites. The observations and the reanalysis data from assimilation experiment were used to investigate the year-to-year changes. As the differences of the reanalysis data (CONC_DA) among years reflect combining effects of meteorology and emission and the differences of modeling result from control experiment (NO_DA, with same emissions) among years reflect the separate effect of meteorology, the important roles of emission and meteorology in driving the changes in the three years can be distinguished and analyzed quantitatively. The analysis indicated that meteorology played different roles in 2016 and 2017: the higher pressure system, lower temperature and higher PBLH in 2016 are favorable for pollution dispersion (compared with 2015) while the situation is almost the opposite in 2017 (compared with 2016) that leads to the increasing PM2.5 from 2016 to 2017 although emission control strategy were implemented in both years. There are still large uncertainties in this approach especially the inaccurate emission input in the model brings large biases in the analysis.

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period from 2000 to 2006 with the Community Land Model, version 3.5 (CLM3.5) integrated with the Parallel Data Assimilation Framework (PDAF) at spatial resolution of 0.0275° (~ 3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans-Ertel Centre for Weather Research (HErZ). Our results show that estimates of soil moisture have improved, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm3/mm3 in open-loop simulations to 0.004 mm3/mm3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.