Showing papers by "Aaron Boone published in 2022"

Interactions between precipitation, evapotranspiration and soil-moisture-based indices to characterize drought with high-resolution remote sensing and land-surface model data

Abstract: Abstract. The Iberian Peninsula is prone to drought due to the high variability in the Mediterranean climate with severe consequences for drinking water supply, agriculture, hydropower and ecosystem functioning. Because of the complexity and relevance of droughts in this region, it is necessary to increase our understanding of the temporal interactions of precipitation, evapotranspiration and soil moisture that originate from drought within the Ebro basin, in northeastern Spain, as the study region. Remote sensing and land-surface models provide high-spatial-resolution and high-temporal-resolution data to characterize evapotranspiration and soil moisture anomalies in detail. The increasing availability of these datasets has the potential to overcome the lack of in situ observations of evapotranspiration and soil moisture. In this study, remote sensing data of evapotranspiration from MOD16A2 and soil moisture data from SMOS1km as well as SURFEX-ISBA land-surface model data are used to calculate the evapotranspiration deficit index (ETDI) and the soil moisture deficit index (SMDI) for the period 2010–2017. The study compares the remote sensing time series of the ETDI and SMDI with the ones estimated using the land-surface model SURFEX-ISBA, including the standardized precipitation index (SPI) computed at a weekly scale. The study focuses on the analysis of the time lags between the indices to identify the synchronicity and memory of the anomalies between precipitation, evapotranspiration and soil moisture. Lag analysis results demonstrate the capabilities of the SPI, ETDI and SMDI drought indices computed at a weekly scale to give information about the mechanisms of drought propagation at distinct levels of the land–atmosphere system. Relevant feedback for both antecedent and subsequent conditions is identified, with a preeminent role of evapotranspiration in the link between rainfall and soil moisture. Both remote sensing and the land-surface model show capability to characterize drought events, with specific advantages and drawbacks of the remote sensing and land-surface model datasets. Results underline the value of analyzing drought with dedicated indices, preferably at a weekly scale, to better identify the quick self-intensifying and mitigating mechanisms governing drought, which are relevant for drought monitoring in semi-arid areas.

Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation – Initialization and Improved Prediction

Abstract: Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-meter temperature over the TP in the LS4P experiment, results from a multi-model ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hot spot” regions identified here; the ensemble means in some “hot spots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Airborne GNSS-R Polarimetric Multiincidence Data Analysis for Surface Soil Moisture Estimation Over an Agricultural Site

Abstract: The objective of this study is to propose a mapping of surface soil moisture (SSM) using airborne measurements based on the GLObal Navigation Satellite System Reflectometry Instrument, a polarimetric instrument. GNSS-R measurements were acquired at the agricultural Urgell site in Spain in July 2021. In situ measurements describing the soil moisture and roughness and the vegetation cover leaf area index were then obtained simultaneously with flight measurements. An analysis of observable copolarization (right–right) reflectivity ${{\boldsymbol{\Gamma}}}_{{\boldsymbol{RR}}}$ and the cross-polarization (right–left) reflectivity $\ {\Gamma }_{RL}$ behaviors as a function of incidence angle is proposed, as is normalization of the reflectivity function of the incidence angle. The sensitivity of reflectivities is then proposed as a function of surface soil moisture. An empirical model with two variables, soil moisture, and the normalized difference vegetation index, based on the principle of the tau–omega model is then considered for the inversion of GNSS-R reflectivity ${\Gamma }_{RL}\ $ and estimation of soil moisture. This model is calibrated and validated by a threefold cross-validation approach. A mapping of SSM at 100 m resolution is created with data from the studied site and three acquired flights.

To the Origin of a Wintertime Screen-level Temperature Bias at High Altitude in a Kilometric NWP Model

Abstract: High-resolution numerical weather prediction (NWP) systems present a strong potential to provide meteorological information in alpine terrain for diverse applications. However, they still suffer from biases highly detrimental for practical purposes. In this study, we investigate the origin of a significant wintertime screen-level temperature bias in forecasts of the AROME-France NWP system in high altitude, snow-covered alpine terrain. For this purpose, a thorough set of meteorological and snow observations from two high altitude instrumental sites is used. Targeted numerical simulations are carried out to disentangle the contributions to this bias coming from atmospheric fields, from the snow scheme and from the coupling between the snowpack and the atmosphere. At both sites, the wind speed and incoming longwave radiation appear significantly negatively biased in AROME in the winter season. Using targeted offline simulations, we show that the simulation errors in these screen-level fields contribute to an average of 67 % of the screen-level temperature bias of AROME, while the contribution of errors in the incoming shortwave radiation is negligible. Additionally, the screen-level temperature of AROME is not majorly impacted by changes in the complexity and especially the vertical layering of the snow model. However, it appears particularly sensitive to the parameterization of turbulent fluxes in stable conditions. Evidence suggest that these findings could at least partially be generalized to the whole AROME-France alpine domain. Hence, reducing the high altitude, winter screen-level temperature bias in AROME may in great part proceed from improving the simulation of atmospheric fields and eliminating some bias compensations in the model.

High-Resolution Reanalysis of Daily Precipitation using AROME Model Over France

Abstract: Among the various meteorological variables, precipitation is one of significant interest, especially for hydrological studies. However, obtaining a reliable precipitation data set is a difficult challenge as precipitation can be very discontinuous in space and time. In this study, a method to obtain a high resolution precipitation reanalysis over France is purposed based on a study from 01/01/2016 to 31/12/2018. The French operational regional model Application de la Recherche à l’Opérationnel à Méso-Echelle (AROME) is combined with precipitation observations, which have been quality controlled, using an optimum interpolation data assimilation algorithm. To use this technique, some hypotheses have to be verified, such as the Gaussian distribution of the innovations. Since the precipitation distribution is highly asymmetric, a Box-Cox transformation is applied to both background and observations to work with variables which behave like Gaussian variables. Then, the background and observation standard deviation errors are determined thanks to the semi-variogram technique, which provides daily values. Results show that the Box-Cox transformation provides better scores for light precipitation and has the same quality as the reference – analysis in the physical space – for high precipitation amounts.

Campagne internationale sur les interactions de la surface terrestre avec l'atmosphère

Abstract: Dédiée aux sciences de l'atmosphère, au climat et à d'autres domaines connexes, tels que l'océanographie ou la glaciologie, La Météorologie, révisée par des pairs et publiée en français, s'adresse aux professionnels de la météo et du climat, aux enseignants, aux étudiants, aux amateurs et aux utilisateurs. La Météorologie a succédé en 1925 à l'Annuaire de la Société météorologique de France (1852-1924) qui avait lui-même succédé à l'Annuaire météorologique de la France (1849-1851).