A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data

In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010, the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification, while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (a few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m<sup>3</sup> m<sup>−3</sup> to 0.087 m<sup>3</sup> m<sup>−3</sup> when using the new formulation in offline experiments, and from 0.110 m<sup>3</sup> m<sup>−3</sup> to 0.088 m<sup>3</sup> m<sup>−3</sup> in operations. It also improves correlations. Additionally, the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiments with the new formulation present drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission

Modelling root water uptake in a complex land surface scheme coupled to a GCM

International audienceThe aim of this paper is to improve the representation of root water uptake in the land surface scheme SECHIBA coupled to the LMD General Circulation Model (GCM). Root water uptake mainly results from the interaction between soil moisture and root profiles. Firstly, one aspect of the soil hydrology in SECHIBA is changed: it is shown that increasing the soil water storage capacity leads to a reduction in the frequency of soil water drought, but enhances the mean evapotranspiration. Secondly, the representation of the soil-vegetation interaction is improved by allowing a different root profile for each type of vegetation. The interaction between sub-grid scale variabilities in soil moisture and vegetation is also studied. The approach consists of allocating a separate soil water column to each vegetation type, thereby 'tiling' the grid square. However, the possibility of choosing the degree of soil moisture spatial heterogeneity is retained. These enhancements of the land surface system are compared within a number of GCM experiments

Comparing regulatory and non-regulatory indices of early childhood education and care (ECEC) quality in the Australian early childhood sector

This study examines associations between Australia’s regulatory ratings of quality in early childhood education and care (ECEC)—the National Quality Standard (NQS)—and two research-based quality rating scales. The analytic sample consisted of 257 ECEC services across three Australian states. Results indicated (1) modest positive associations between NQS ratings and scale scores; (2) some specificity between NQS quality areas (educational programs and practice; relationships with children) and one research scale—the Sustained Shared Thinking and Emotional Wellbeing (SSTEW) scale; (3) variability in quality scales scores within each NQS designation; and (4) mitigation of these associations when the time-gap between ratings exceeded 24 months. Findings suggest NQS and research scales tap some common core of quality, yet capture different aspects of quality, suggesting both could be used to raise standards of quality in Australian preschools, where the research scales potentiate raising quality to even higher levels than NQS

How well do operational Numerical Weather Prediction configurations represent hydrology?

Land surface models (LSMs) have traditionally been designed to focus on providing lower boundary conditions to the atmosphere with less focus on hydrological processes. State of the art application of LSMs include land data assimilation system (LDAS) which incorporates available land surface observations to provide an improved realism of surface conditions. While improved representations of the surface variables (such as soil moisture and snow depth) make LDAS an essential component of any Numerical Weather Prediction (NWP) system, the related increments remove or add water, potentially having a negative impact on the simulated hydrological cycle by opening the water budget. This paper focuses on evaluating how well global NWP configurations are able to support hydrological applications, in addition to the traditional weather forecasting. River discharge simulations from two climatological reanalyses are compared: one ‘online’ set which includes land-atmosphere coupling and LDAS with an open water budget, and also an ‘offline’ set with a closed water budget and no LDAS. It was found that while the online version of the model largely improves temperature and snow depth conditions, it caused poorer representation of peak river flow, particularly in snowmelt-dominated areas in the high latitudes. Without addressing such issues there will never be confidence in using LSMs for hydrological forecasting applications across the globe. This type of analysis should be used to diagnose where improvements need to be made; considering the whole Earth System in the data assimilation and coupling developments is critical for moving towards the goal of holistic Earth System approaches

Trends in the GloFAS-ERA5 river discharge reanalysis

The main objective of this study is to analyse the GloFAS-ERA5 river discharge reanalysis for any noticeable change (including gradual trends or discontinuities) in the annual mean time series across the 1979-2018 (40-year) period, and to evaluate how realistic these are compared with available observed river discharge time series. These variabilities are quantified by linear regression in order to highlight any concerning features in the GloFAS-ERA5 time series. This work is particularly important for GloFAS, as large trends, discontinuities or other similar features could have a major consequence on the GloFAS flood thresholds in around 50% of catchments, which are based on GloFAS-ERA5, and thus subsequently on the issuing of flood warnings. In addition, this study also contributes to the understanding of the water cycle variable behaviour in ERA5 (driver of GloFAS-ERA5) and ERA5-Land (higher resolution land reanalysis forced by ERA5, produced offline) by exploring the linear trends in river discharge and related hydrological variables. In exploring the stability of the time series in ERA5, we seek to trigger potential further discussions and research studies, which subsequently should help with the planning and development for the next generation ECMWF reanalysis, ERA6

Measuring interactional quality in pre-school settings: Introduction and validation of the Sustained Shared Thinking and Emotional Wellbeing (SSTEW) scale

Research increasingly acknowledges the importance of high quality interactions that support and extend children’s thinking. Few measurement tools currently exist, however, to capture this specific aspect of process quality. The Sustained Shared Thinking and Emotional Wellbeing (SSTEW) scale was developed to assess interactional quality in early childhood education and care, and it includes dimensions of process quality based on developmental theories and practice in effective settings. This study compared ratings on the SSTEW and Early Childhood Environment Rating Scale – Extension (ECERS-E) to consider the impact of varying levels of curricular and interactional quality on child development in 45 Australian pre-school centres; namely the language, numeracy and socio-behavioural development of 669 children at the end of their pre-school year. Results indicated a level of predictive validity for interactional quality ratings as measured by SSTEW which, while related to curricular quality ratings on ECERS-E, differed in associations across domains of child development

Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France

The SMOSMANIA soil moisture network in Southwestern France is used to evaluate modelled and remotely sensed soil moisture products. The surface soil moisture (SSM) measured in situ at 5 cm permits to evaluate SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km) active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP), issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km gridspacing) by TU-Wien (Vienna University of Technology) over a two year period (2007–2008). A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP) and the Integrated Forecasting System (IFS) analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses

ERA-Interim/Land: a global land surface reanalysis data set

ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models

Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau

The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the source of many major rivers in Asia. An accurate long-range (i.e. seasonal) meteorological forecast is of great importance for this region. The fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5) provides global long-range meteorological forecasts including over the TP. However, SEAS5 uses land initial conditions produced by assimilating Interactive Multisensor Snow and Ice Mapping System (IMS) snow data only below 1500 m altitude, which may affect the forecast skill of SEAS5 over mountainous regions like the TP. To investigate the impacts of snow assimilation on the forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer 2018. Significant changes occur in the springtime. Without snow assimilation, the reforecasts overestimate snow cover and snow depth while underestimating daily temperature over the TP. Compared to satellite-based estimates, precipitation reforecasts perform better in the west TP (WTP) than in the east TP (ETP). With snow assimilation, the reforecasts of snow cover, snow depth and temperature are consistently improved in the TP in the spring. However, the positive bias between the precipitation reforecasts and satellite observations worsens in the ETP. Compared to the experiment with no snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the ETP and around the longitude 95∘ E. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the effects of a more realistic, decreased snowpack, providing favourable conditions for generating more precipitation. Overall, snow assimilation can improve seasonal forecasts through the interaction between land and atmosphere.</p