Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.

Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.

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Abstract. For several years, global warming has been unequivocal, leading to climate change at global, regional and local scales. A good understanding of climate characteristics and local variability is important for adaptation and response. Indeed, the contribution of local processes and their understanding in the context of warming are still very little studied and poorly represented in climate models. Improving the knowledge of surface–atmosphere feedback effects at local scales is therefore important for future projections. Using observed data in the Paris region from 1979 to 2017, this study characterizes the changes observed over the last 40 years for six climatic parameters (e.g. mean, maximum and minimum air temperature at 2 m, 2 m relative and specific humidities and precipitation) at the annual and seasonal scales and in summer, regardless of large-scale circulation, with an attribution of which part of the change is linked to large-scale circulation or thermodynamic. The results show that some trends differ from the ones observed at the regional or global scale. Indeed, in the Paris region, the maximum temperature increases faster than does the minimum temperature. The most significant trends are observed in spring and in summer, with a strong increase in temperature and a very strong decrease in relative humidity, while specific humidity and precipitation show no significant trends. The summer trends can be explained more precisely using large-scale circulation, especially regarding the evolution of the precipitation and specific humidity. The analysis indicates the important role of surface–atmosphere feedback in local variability and that this feedback is amplified or inhibited in a context of global warming, especially in an urban environment.

For several years, global warming has been unequivocal, leading to climate change at global, regional and local scales. A good understanding of climate characteristics and local variability is important for adaptation and response. Indeed, the contribution of local processes and their understanding in the context of warming are still very little studied and poorly represented in climate models. Improving the knowledge of surface-atmosphere feedback effects at local scales is therefore important for future projections. Using observed data in the Paris region from 1979 to 2017, this study characterizes the changes observed over the last 40 years for six climatic parameters (e.g. mean, maximum and minimum air temperature at 2 m, 2 m relative and specific humidities and precipitation) at the annual and seasonal scales and in summer, regardless of large-scale circulation, with an attribution of which part of the change is linked to large-scale circulation or thermodynamic. The results show that some trends differ from the ones observed at the regional or global scale. Indeed, in the Paris region, the maximum temperature increases faster than does the minimum temperature. The most significant trends are observed in spring and in summer, with a strong increase in temperature and a very strong decrease in relative humidity, while specific humidity and precipitation show no significant trends. The summer trends can be explained more precisely using large-scale circulation, especially regarding the evolution of the precipitation and specific humidity. The analysis indicates the important role of surface-atmosphere feedback in local variability and that this feedback is amplified or inhibited in a context of global warming, especially in an urban environment.

Abstract. Local short-term temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy balance terms, which are well known but whose specific contribution and importance on the hourly scale still need to be further analyzed. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective being to estimate their importance in hourly near-surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloudy-sky radiation), atmospheric heat exchange, ground heat exchange, and advection. Compared to direct measurements of hourly temperature variations, it is shown that the sum of the four terms gives a good estimate of the hourly temperature variations, allowing a better assessment of the contribution of each term to the variation, with an accurate diurnal and annual cycle representation, especially for the radiative terms. A random forest analysis shows that whatever the season, clouds are the main modulator of the clear-sky radiation for 1 h temperature variations during the day and mainly drive these 1 h temperature variations during the night. Then, the specific role of clouds is analyzed exclusively in cloudy conditions considering the behavior of some classical meteorological variables along with lidar profiles. Cloud radiative effect in shortwave and longwave and lidar profiles show a consistent seasonality during the daytime, with a dominance of mid- and high-level clouds detected at the SIRTA observatory, which also affects near-surface temperatures and upward sensible heat flux. During the nighttime, despite cloudy conditions and having a strong cloud longwave radiative effect, temperatures are the lowest and are therefore mostly controlled by larger-scale processes at this time.

<p>The local contribution of clouds to the surface energy balance and temperature variability is an important topic in order to apprehend how this intake affects local climate variability and extreme events, how this contribution varies from one place to another, and how it evolves in a warming climate. The scope of this study is to understand how clouds impact temperature variability, to quantify their contribution, and to compare their effects to other surface processes. To do so, we develop a method to estimate the different terms that control temperature variability at the surface (∂T<sub>2m</sub> /∂t) by using this equation: <strong>∂T<sub>2m</sub> /∂t=R+HA+HG+Adv</strong> where R is the radiation that is separated into the cloud term (R<sub>cloud</sub>) and the clear sky one (R<sub>CS</sub>), HA the atmospheric heat exchange, HG the ground heat exchange, and Adv the advection. These terms are estimated hourly, almost only using direct measurements from SIRTA-ReOBS dataset (an hourly long-term multi-variables dataset retrieved from SIRTA, an observatory located in a semi-urban area 20-km South-West of Paris; Chiriaco et al., 2019) for a five-years period. The method gives good results for the hourly temperature variability, with a 0.8 correlation coefficient and a weak residual term between left part (directly measured) and right part of the equation.</p><p>A bagged decision trees analysis of this equation shows that R<sub>CS</sub> dominates temperature variability during daytime and is mainly modulated by cloud radiative effect (R<sub>cloud</sub>). During nighttime, the bagged decision trees analysis determines that R<sub>cloud</sub> is the term controlling temperature changes. When a diurnal cycle analysis (split into seasons) is performed for each term, HA becomes an important negative modulator in the late afternoon, chiefly in spring and summer, when evaporation and thermal conduction are increased. In contrast, HG and Adv terms do not play an essential role on temperature variability at this temporal scale and their contribution is barely considerable in the one-hour variability, but still they remain necessary in order to obtain the best coefficient estimator between the directly measured observations and the method estimated. All terms except advection have a marked monthly-hourly cycle.</p><p>Next steps consist in characterize the types of clouds and study their physical properties corresponding to the cases where R<sub>cloud</sub> is significant, using the Lidar profiles also available in the SIRTA-ReOBS dataset.</p>

Abstract. Local temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy budget terms, which depend mostly on radiation availability and thus cloud processes. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective to estimate their importance in hourly surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloud radiation), atmospheric heat exchange, ground heat exchange, and advection. Compared to direct measurements of hourly temperature variations, it is shown that the sum of the four terms gives a good estimate of the hourly temperature variations, allowing a better assessment of the contribution of each term to the variation, with an accurate diurnal and annual cycles representation, especially for the radiative terms. A random forest analysis shows that whatever the season, clouds are the main modulator of the clear sky radiation for 1-hour temperature variations during the day, and mainly drive these 1-hour temperature variations during the night. Then, the specific role of clouds is analyzed exclusively in cloudy conditions considering the behavior of some classical meteorological variables along with lidar profiles. Cloud radiative effect in shortwave and longwave and lidar profiles show a consistent seasonality during the daytime, with a dominance of mid- and high-level clouds detected at the SIRTA observatory, which also affects surface temperatures and upward sensible heat flux. During the nighttime, despite cloudy conditions and having a strong cloud longwave radiative effect, temperatures are the lowest and are therefore mostly controlled by larger-scale processes at this time.