This paper proposes a method based on a local weather type classification approach to facilitate analysis and communication of climate information in local climate studies. Presented herein is an application to urban climatology in Toulouse, France, but the method can be used in other applied fields of climatology as well. To describe the climatic context of this urbanized area, the local weather types that explain the plurality of weather situations Toulouse faces are presented in depth. In order to show the potential for use of this approach, this information is applied to the study of changes in local weather types in terms of frequency and intensity within a series of future climate projections, a classic urban canopy and a series of atmospheric boundary layer analyses, and as a support for communication aimed to initiate urban climate awareness in urban planning practices. The proposed classification method has been coded in an R script and is provided as a supporting information file. The paper concludes that a systematic pre-study using this kind of climatic analysis is a good practice for performing climatic contextualization in local scale applied studies, both for scientific analysis and communication.
High-resolution maps of the urban heat island (UHI) and building energy consumption are relevant for urban planning in the context of climate change mitigation and adaptation. A statistical-dynamical downscaling for these parameters is proposed in the present study. It combines a statistical local weather type approach with dynamical simulations using the mesoscale atmospheric model Meso-NH coupled to the urban canopy model Town Energy Balance. The downscaling is subject to uncertainties related to the weather type approach (statistical uncertainty) and to the numerical models (dynamical uncertainty). These uncertainties are quantified for two French cities (Toulouse and Dijon) for which long-term dense high-quality observations are available. The seasonal average nocturnal UHI intensity is simulated with less than 0.2 K bias for Dijon, but it is overestimated by up to 0.8 K for Toulouse. The sensitivity of the UHI intensity to weather type is, on average, captured by Meso-NH. The statistical uncertainty is as large as the dynamical uncertainty if only one day is simulated for each weather type. It can be considerably reduced if 3-6 days are taken instead. The UHI reduces the building energy consumption by 10% in the center of Toulouse; it should therefore be taken into account in the production of building energy consumption maps.
<p>Improving streamflow forecasts helps in reducing socio-economical impacts of hydrological-related damages. Among them, improving hydropower production is a challenge, even more so in a context of climate change.<strong> </strong>Deep learning models drew the attention of scientists working on forecasting models based on physical laws, since they got recognition in other domains. Artificial Neural Network (ANN) offer promising performance for streamflow forecasts, including good accuracy and lesser time to run compared to traditional physically-based models.&#160;</p><p>&#160;</p><p>The objective of this study is to compare different spatial discretization schemes of inputs in an ANN model for streamflow forecast. The study focuses on the &#8220;Au Saumon&#8221; watershed in Southern Quebec (Canada) during summer periods, with a forecast window of 7 days at a daily timestep. Parameterization of the ANN was a key preliminary step: the number of neurons in the hidden layer was first optimized, leading to 6 neurons. The model was trained on a 11-year dataset (2000-2005 and 2007-2011) followed by model validation on one dry (2012) and one wet (2006) year to take into account extreme hydrologic regimes.&#160;</p><p>&#160;</p><p>To lead this study, the physically-based hydrological &#8216;Hydrotel&#8217; model is the reference to compare our results. The model defines watershed heterogeneity using hydrological units based on land uses, soil types, and topography, called Relative Homogeneous Hydrological Units (RHHU). The Nash-Sutcliffe Efficiency score (NSE) is the main evaluation criteria calculated. In a preliminary step, we have to ensure the ANN model can satisfactorily mimic Hydrotel. With the same model inputs, that is same variables and same spatial discretizations of variables (total precipitation, daily maximum and minimum temperatures, and soil surface humidity), the ANN forecasts were found to be better than those of Hydrotel for one to 7-day forecasts.&#160;</p><p>&#160;</p><p>Three different watershed spatial discretizations were tested: global, fully distributed, and semi-distributed. For the global model, hydrometeorological data used as inputs to the ANN model were averaged across all RHHUs. The complexity is reduced with loss of spatial details and heterogeneity. For the fully distributed model, a regular grid was defined with six cells of 28x28km2 covering all the watershed. For the semi-distributed model, spatial distribution of the input data was that of the RHHUs. For this discretization, the state variables (soil moisture and outflow) were updated at each forecast timestep, whether on all RHHUs, or only on the RHHU of the outlet.</p><p>&#160;</p><p>Depending on the spatial discretization of inputs used, the accuracy differed. The fully distributed model offered the least performance, with NSE values of 0.85 ,while the global model surprisingly performed better with a 0.93 NSE. Moreover, updating soil moisture on all the RHHUs of the semi-distributed model improved the NSE across the entire window of forecast.</p><p>This research will assess the ANN model performance developed using ERA5-land precipitation and temperature reanalysis and ground observations of soil moisture. Given the promising results obtained with the fully and semi distributed models, our ANN model will be tested with state variables retrieved from satellite data, such as surface soil moisture from SMAP and SMOS missions.</p><p><br><br></p>
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