Implications For Data Assimilation Research Articles

Exploring the characteristics of Fengyun-4A Advanced Geostationary Radiation Imager (AGRI) visible reflectance using the China Meteorological Administration Mesoscale (CMA-MESO) forecasts and its implications for data assimilation

Abstract. The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun (FY)-4A geostationary satellite has provided high-spatiotemporal-resolution visible reflectance data since 12 March 2018. Data assimilation experiments under the framework of observing system simulation experiments have shown the great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To assimilate the AGRI visible reflectance in real-world cases, it is important to evaluate the quality and to quantify the observation errors in these data. In this study, the FY-4A AGRI channel 2 (0.55–0.75 µm) reflectance data (O) were compared with the equivalents (B) derived from the short-term forecasts of the China Meteorological Administration Mesoscale (CMA-MESO) model using the Radiative Transfer for the Television Infrared Observation Satellite Operational Vertical Sounder (RTTOV, v12.3). It is shown that the O–B biases could be used to reveal the abrupt change related to the measurement calibration processes. In general, the O–B departure was positively biased in most cases. Potential causes include the deficiencies of the NWP model, the forward-operator errors, and the unresolved aerosol processes. The relative biases of O–B computed from cloud-free and cloudy pixels were used to correct the systematic biases for the corresponding scenarios over land and sea surfaces separately. In general, the method effectively reduced the O–B biases. Moreover, the bias-correction method based on an ensemble forecast is more robust than a deterministic forecast due to the advantages of the former in dealing with uncertainties in cloud simulations. The findings demonstrate that analyzing the O–B biases has a potential to monitor the performance of the FY-4A AGRI visible instrument and to correct the systematic biases in the observations, which will facilitate the assimilation of these data in conventional data assimilation applications.

Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting

Solar activity, ranging from the background solar wind to energetic coronal mass ejections (CMEs), is the main driver of the conditions in the interplanetary space and in the terrestrial space environment, known as space weather. A better understanding of the Sun-Earth connection carries enormous potential to mitigate negative space weather effects with economic and social benefits. Effective space weather forecasting relies on data and models. In this paper, we discuss some of the most used space weather models, and propose suitable locations for data gathering with space weather purposes. We report on the application of Representer analysis (RA) and Domain of Influence (DOI) analysis to three models simulating different stages of the Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar wind - magnetosphere interaction, and the PLUTO model, used to simulate CME propagation in interplanetary space. Our analysis is promising for space weather purposes for several reasons. First, we obtain quantitative information about the most useful locations of observation points, such as solar wind monitors. For example, we find that the absolute values of the DOI are extremely low in the magnetospheric plasma sheet. Since knowledge of that particular sub-system is crucial for space weather, enhanced monitoring of the region would be most beneficial. Second, we are able to better characterize the models. Although the current analysis focuses on spatial rather than temporal correlations, we find that time-independent models are less useful for Data Assimilation activities than time-dependent models. Third, we take the first steps towards the ambitious goal of identifying the most relevant heliospheric parameters for modelling CME propagation in the heliosphere, their arrival time, and their geoeffectiveness at Earth.

Towards variational retrieval of warm rain from passive microwave observations

Abstract. An experimental retrieval of oceanic warm rain is presented, extending a previous variational algorithm to provide a suite of retrieved variables spanning non-raining through predominantly warm raining conditions. The warm rain retrieval is underpinned by hydrometeor covariances and drizzle onset data derived from CloudSat. Radiative transfer modelling and analysis of drop size variability from disdrometer observations permit state-dependent observation error covariances that scale with columnar rainwater during iteration. The state-dependent errors and nuanced treatment of drop distributions in precipitating regions are novel and may be applicable for future retrievals and all-sky data assimilation methods. This retrieval method can effectively increase passive microwave sensors' sensitivity to light rainfall that might otherwise be missed. Comparisons with space-borne and ground radar estimates are provided as a proof of concept, demonstrating that a passive-only variational retrieval can be sufficiently constrained from non-raining through warm rain conditions. Significant deviations from forward model assumptions cause non-convergence, usually a result of scattering hydrometeors above the freezing level. However, for cases with liquid-only precipitation, this retrieval displays greater sensitivity than a benchmark operational retrieval. Analysis against passive and active products from the Global Precipitation Measurement (GPM) satellite shows substantial discrepancies in precipitation frequency, with the experimental retrieval observing more frequent light rain. This approach may be complementary to other precipitation retrievals, and its potential synergy with the operational passive GPM retrieval is briefly explored. There are also implications for data assimilation, as all 13 channels on the GPM Microwave Imager (GMI) are simulated over ocean with fidelity in warm raining conditions.

Linear Error Dynamics for Turbulent Flow in Urban Street Canyons

AbstractThe ability to make forecasts depends on atmospheric predictability and the growth of errors. It has recently been shown that the predictability of urban boundary layers differs in important respects from that of the free atmosphere on the mesoscale and larger; in particular, nonlinearity may play a less prominent role in the error evolution. This paper investigates the applicability of linear theory to the error evolution in turbulent street-canyon flow. Using large-eddy simulation, streamwise aspect ratios between 0.15 and 1.50, and identical-twin experiments, it is shown that the growth rate of the error kinetic energy can be estimated from Eulerian averages and that linear theory provides insight into the spatial structure of the error field after saturation. The results should be applicable to cities with deep and closely spaced canyons. Implications for data assimilation and modeling are discussed.

For How Long Should What Data Be Assimilated for the Mesoscale Forecasting of Convection and Why? Part I: On the Propagation of Initial Condition Errors and Their Implications for Data Assimilation

Abstract Data assimilation is used among other things to constrain the initial conditions of weather forecasting models by fitting the model fields to observations made over a certain time interval. In particular, it tries to tie incomplete data with model constraints to detect and correct for initial condition errors. This is possible only if initial condition errors leave their signature on the data assimilated and if the model is capable of faithfully reproducing such signatures. Using simulations of the evolution of convective storms in the Great Plains over an active 6-day period, the propagation of initial condition errors to other variables as well as their effect on the accuracy of the forecasts were investigated. Increasing the assimilation time window boosts the ability of assimilation systems to detect a variety of initial condition errors; however, limits to the predictability of convective events impose a maximum assimilation period that is a function of the type of measurements assimilated as well as of the type of errors one tries to correct for. These findings are then used to suggest changes in assimilation approaches to take into account the different predictability times of the model fields constrained by assimilation.

Effects of high‐frequency wind sampling on simulated mixed layer depth and upper ocean temperature

Effects of high‐frequency wind sampling on a near‐global ocean model are studied by forcing the model with a 12 hourly averaged wind product and its 24 hourly subsamples in separate experiments. The differences in mixed layer depth and sea surface temperature resulting from these experiments are examined, and the underlying physical processes are investigated. The 24 hourly subsampling not only reduces the high‐frequency variability of the wind but also affects the annual mean wind because of aliasing. While the former effect largely impacts mid‐ to high‐latitude oceans, the latter primarily affects tropical and coastal oceans. At mid‐ to high‐latitude regions the subsampled wind results in a shallower mixed layer and higher sea surface temperature because of reduced vertical mixing associated with weaker high‐frequency wind. In tropical and coastal regions, however, the change in upper ocean structure due to the wind subsampling is primarily caused by the difference in advection resulting from aliased annual mean wind, which varies with the subsampling time. The results of the study indicate a need for more frequent sampling of satellite wind measurement and have implications for data assimilation in terms of identifying the nature of model errors.

Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation

Abstract Three independent surface soil moisture datasets for the period 1979–87 are compared: 1) global retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), 2) global soil moisture derived from observed meteorological forcing using the NASA Catchment Land Surface Model, and 3) ground-based measurements in Eurasia and North America from the Global Soil Moisture Data Bank. Time-average soil moisture fields from the satellite and the model largely agree in the global patterns of wet and dry regions. Moreover, the time series and anomaly time series of monthly mean satellite and model soil moisture are well correlated in the transition regions between wet and dry climates where land initialization may be important for seasonal climate prediction. However, the magnitudes of time-average soil moisture and soil moisture variability are markedly different between the datasets in many locations. Absolute soil moisture values from the satellite and the model are very different, and neither agrees...

A note on the vertical scales of temperature and salinity and their signature in dynamic height in the western Pacific Ocean: Implications for data assimilation

The relationship between the vertical structures of ocean temperature and salinity and their concurrent signature in surface dynamic height anomalies are investigated along the 165°E transequatorial section in the western Pacific Ocean. The data are from conductivity‐temperature depth casts made during 28 oceanographic cruises mainly conducted during the 1984–1992 time period as part of the TOGA program. The analysis is based on empirical orthogonal functions that isolate the primary modes of variability, and some possible physical interpretations are suggested. The results show that the first six modes, explaining roughly 80% of the variance, display distinct vertical scales and exhibit coherent and observable signatures in dynamic height which reveal complex patterns in latitude and time. In addition, a method has been developed to reconstruct the temperature and salinity profiles from their signature in the surface dynamic height anomaly using the dominant EOFs. The method is new in the sense that no recourse is made to T–S relations. Residual errors in the vertical after reconstruction are lower than the intrinsic variability, and the method can successfully reproduce the variability at ENSO timescales. The residual error in dynamic height anomalies is lower than 1 dyn. cm in the equatorial belt, and of the order of 2–4 dyn. cm in the subtropics. The results demonstrate that sea level observations may be translate into temperature and salinity anomalies at depth. The implications for data assimilation in ocean models are discussed.