System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction(SNAP):System Formulation and Preliminary Evaluation

A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.

Impacts of Multigrid NLS-4DVar-based Doppler Radar Observation Assimilation on Numerical Simulations of Landfalling Typhoon Haikui (2012)

We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Forecasting(WRF)model.Observation data included radial velocity(Vr)and reflectivity(Z)data from a single Doppler radar,quality controlled prior to assimilation.Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods.Compared with a forecast that began with NCEP analysis data,our radar data assimilation results were clearly improved in terms of structure,intensity,track,and precipitation prediction for Typhoon Haikui(2012).The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method,but that the latter was more efficient.The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity,track,and precipitation;however,the impacts of Vr data were significantly greater that those of Z data.Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.

针对中国陆地碳汇反演的大气二氧化碳浓度观测网络最优化设计

精准估算陆地生态系统碳汇对中国制定碳减排措施、实现碳中和目标有重要科学支撑作用.大气反演方法基于大气二氧化碳浓度观测数据,可有效估算地面二氧化碳通量的时空分布.目前中国陆地生态系统碳汇的大气反演估算仍有巨大不确定性,其中一个重要瓶颈在于缺乏足够的二氧化碳观测站点.本文研发了针对中国区域的大气反演框架,提出了二氧化碳观测网络的站点布设方案以最大程度降低中国陆地碳汇反演估算的不确定性.研究表明,在现有站点基础上,亟需在生长季植被生产力较高的东南、东北、华北和青藏高原地区增设二氧化碳观测站点.若在中国建设30个大气二氧化碳观测站点可将碳汇估算不确定性从每年10亿吨碳降低至3亿吨碳;建设60个站点可将不确定性进一步降低至每年2亿吨碳.这些站点将是“天-空-地”综合碳观测系统的重要组成部分,服务于中国二氧化碳收支反演和精准核算.

Constraint of satellite CO_(2)retrieval on the global carbon cycle from a Chinese atmospheric inversion system

Satellite carbon dioxide(CO_(2))retrievals provide important constraints on surface carbon fluxes in regions that are undersampled by global in situ networks.In this study,we developed an atmospheric inversion system to infer CO_(2)sources and sinks from Orbiting Carbon Observatory-2(OCO-2)column CO_(2)retrievals during 2015–2019,and compared our estimates to five other state-of-the-art inversions.By assimilating satellite CO_(2)retrievals in the inversion,the global net terrestrial carbon sink(net biome productivity,NBP)was found to be 1.03±0.39 petagrams of carbon per year(Pg C yr^(-1));this estimate is lower than the sink estimate of 1.46–2.52 Pg C yr^(-1),obtained using surface-based inversions.We estimated a weak northern uptake of 1.30 Pg C yr-1and weak tropical release of-0.26 Pg C yr^(-1),consistent with previous reports.By contrast,the other inversions showed a strong northern uptake(1.44–2.78 Pg C yr-1),but diverging tropical carbon fluxes,from a sink of 0.77 Pg C yr^(-1) to a source of-1.26 Pg C yr^(-1).During the 2015–2016 El Ni?o event,the tropical land biosphere was mainly responsible for a higher global CO_(2)growth rate.Anomalously high carbon uptake in the northern extratropics,consistent with concurrent extreme Northern Hemisphere greening,partially offset the tropical carbon losses.This anomalously high carbon uptake was not always found in surface-based inversions,resulting in a larger global carbon release in the other inversions.Thus,our satellite constraint refines the current understanding of flux partitioning between northern and tropical terrestrial regions,and suggests that the northern extratropics acted as anomalous high CO_(2)sinks in response to the 2015–2016 El Nino event.