Near-surface Temperature Fields Research Articles

Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model

The analysis of the near-surface air temperature is vital for many applications such as urban heat islands and climate change studies. In particular, extreme weather events are typically localized and so should the corresponding adaptation measures. However, climate scientists are often confronted with the difficulty of providing reliable predictions at high spatial resolutions in the order of 1 km. We propose to train a convolutional neural network model to emulate the hourly high-resolution near-surface air temperature field simulated by the Weather Research and Forecasting (WRF) software over a period of 18 months. The model is driven by current and past lags of coarse SEVIRI land surface temperature fields. This nowcasting application implements a downscaling of the spatial resolution of the input by about a factor of four, while establishing a correlation between current and past land surface temperature maps and the current near-surface air temperature field. The U-Net variant that is proposed in this study uses regularization to prevent over-fitting and implements a novel space-time approach, where multiple time steps are fed into the model through 3D convolution layers. Besides LST, the model also uses urban density as additional static input to be able to predict the temperature more accurately in urban areas and to improve the generalizability of the trained model. The performance of our U-Net model is assessed via comparison to an MLR benchmark (ridge regularization). The model is shown to be superior on all performance metrics. It achieves a mean absolute error of 1.36 °C versus 1.49 °C for benchmark (a 9% relative improvement) and a root mean square error of 1.77 °C versus 1.91 °C for benchmark (a 7% relative improvement). The absolute error of the model is less than 2 °C for 77% of the prediction points versus 72% for the benchmark (a 7% relative improvement). The improvement over the benchmark is even more significant during extreme hot periods. We demonstrate the generalizability of the approach by testing the trained model on unseen spatial domains.

Impacts of an Urban Canopy Scheme and Surface Observation Data on a Heavy Rain Event through Data Assimilation

Considering urbanization effects on atmospheric states and subsequent precipitation is crucial to improve the accuracy of forecasting localized heavy rainfall around urban areas and to mitigate related disasters. For that purpose, it is effective to use a time development model that can accurately represent city-specific effects, such as urban heat island effect, in the assimilation process, and to assimilate high-frequency/high-density surface observation data that have not been used thus far. Therefore, this study incorporated a forecast model with an urban canopy scheme into an ensemble-based assimilation system and assimilated dense surface data from an Atmospheric Environmental Regional Observation System. Then, we performed analysis-forecast experiments for a heavy rain event in Tokyo metropolitan area on August 30, 2017, to examine the impact of urbanization. Our results showed that the urban scheme and surface observation improved near-surface temperature and moisture fields, thereby contributing to the formation of a clearer convergence line between the easterly and southerly winds where it was observed. Consequently, these improvements resulted in an earlier onset of rainfall and better reproduction of the heavy rainfall distribution.

Impact of Global Warming on Zero Degree Isotherm of Near Surface Temperature

The evaluation of the modern global warming impact on the zero degree isotherm crossing dates of near-surface temperature is unequivocally determined by the length of the warm (cold) season in a given region. The study was conducted using perennial, near-surface temperature field data from ten observation points in a complex, highland region. The correlation changes of the dates of the crossing of the zero degree isotherm of temperature concerning the vertical and horizontal displacement is studied. The statistical structure of the multi-year change in the dates is established. It is accepted that the increase in the warm season caused by global warming mainly occurs in the first half of the year when the zero-degree isotherm crosses from negative to positive. There are also rare cases when the warm season, for a long time, experiences a decrease in the opposite.

Network-Based Approach and Climate Change Benefits for Forecasting the Amount of Indian Monsoon Rainfall

Abstract Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year’s data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill.

Short-term probabilistic forecasting of meso-scale near-surface urban temperature fields

This paper introduces a probabilistic approach to spatio-temporal high resolution meso-scale modeling of near-surface temperature and applies it to regions of dimension about 150∼200 km, with 1 km grid spacing and 30-min interval. Our probabilistic approach, based on linear Gaussian models and dimensionality reduction, can accurately forecast short-term temperature fields and serve as a computationally less expensive alternative to physics-based models that necessitate high-performance computing. The probabilistic models here are calibrated from simulations of a physics-based model, the Princeton Urban Canopy Model, coupled to the Weather Research and Forecasting Model (WRF-PUCM). We assess the performance of the calibrated models to forecast short-term near-surface temperature in various cases. In the numerical campaign, our models achieve 0.97–1.13 °C root mean squared error (RMSE) for 24-hours ahead forecast; generating three days of forecast takes between 20 and 170 sec on a single processor (Intel Xeon E5-2690 v4@2.60GHz). Hence, the proposed approach provides predictions at relatively high accuracy and low computational cost.

Frequency-Dependent Estimation of Effective Spatial Degrees of Freedom

AbstractClimate variability occurs over wide ranges of spatial and temporal scales. It exhibits a complex spatial covariance structure, which depends on geographic location (e.g., tropics vs extratropics) and also consists of a superposition of (i) components with gradually decaying positive correlation functions and (ii) teleconnections that often involve anticorrelations. In addition, there are indications that the spatial covariance structure depends on frequency. Thus, a comprehensive assessment of the spatiotemporal covariance structure of climate variability would require an extensive set of statistical diagnostics. Therefore, it is often desirable to characterize the covariance structure by a simple summarizing metric that is easy to compute from datasets. Such summarizing metrics are useful, for example, in the context of comparisons between climate models or between models and observations. Here we introduce a frequency-dependent version of a simple measure of the effective spatial degrees of freedom. The measure is based on the temporal variance of the global average of some climate variable, and its novel aspect consists in its frequency dependence. We also provide a clear geometric interpretation of the measure. Its easy applicability is demonstrated using near-surface temperature and precipitation fields obtained from a paleoclimate model simulation. This application reveals a distinct scaling behavior of the spatial degrees of freedom as a function of frequency, ranging from monthly to millennial scales.

Assessment of the Canary current upwelling system in a regionally coupled climate model

The Canary current upwelling is one of the major eastern boundary coastal upwelling systems in the world, bearing a high productive ecosystem and commercially important fisheries. The Canary current upwelling system (CCUS) has a large latitudinal extension, usually divided into upwelling zones with different characteristics. Eddies, filaments and other mesoscale processes are known to have an impact in the upwelling productivity, thus for a proper representation of the CCUS and high horizontal resolution are required. Here we assess the CCUS present climate in the atmosphere–ocean regionally coupled model. The regional coupled model presents a global oceanic component with increased horizontal resolution along the northwestern African coast, and its performance over the CCUS is assessed against relevant reanalysis data sets and compared with an ensemble of global climate models (GCMs) and an ensemble of atmosphere-only regional climate models (RCMs) in order to assess the role of the horizontal resolution. The coupled system reproduces the larger scale pattern of the CCUS and its latitudinal and seasonal variability over the coastal band, improving the GCMs outputs. Moreover, it shows a performance comparable to the ensemble of RCMs in representing the coastal wind stress and near-surface air temperature fields, showing the impact of the higher resolution and coupling for CCUS climate modelling. The model is able of properly reproducing mesoscale structures, being able to simulate the upwelling filaments events off Cape Ghir, which are not well represented in most of GCMs. Our results stress the ability of the regionally coupled model to reproduce the larger scale as well as mesoscale processes over the CCUS, opening the possibility to evaluate the climate change signal there with increased confidence.

Intraseasonal SST\u2013precipitation relationship in a coupled reanalysis experiment using the MRI coupled atmosphere\u2013ocean data assimilation system

To evaluate the atmosphere–ocean coupled data assimilation system developed at the Meteorological Research Institute, the lead-lag relation between the intraseasonal variations (with a time scale of 20–100 days) in precipitation and sea surface temperature (SST) is examined in the tropics. It is shown that the relationship over the tropical western Pacific in the coupled reanalysis experiment (CDA) follows the observed relationship more closely than that in the uncoupled reanalysis experiment (UCPL). However, the lead-lag correlations with the observed SST are almost identical between precipitations in CDA and UCPL, indicating that the atmospheric component is strongly constrained by atmospheric observations and hardly affected by the SSTs as boundary conditions. Better representation of the SST–precipitation relationship in CDA is, thus, mostly due to the SST variation modified by the model physics. Comparison with additional reanalysis experiments using coupled and uncoupled systems that assimilate only in-situ observations without satellite observations suggests that the coupled model's physics complements the relatively weak observation constraints and reduces the degradation of the SST–precipitation relationship. Additional analysis for CDA suggests that the warming-to-cooling (cooling-to-warming) transition of the surface net flux, which is in phase with precipitation, is delayed from the positive (negative) peak of SST due to downward heat propagation in the ocean. Comparison of the oceanic near-surface temperature field with observation data indicates that the downward propagation of heat signals is too fast in CDA, resulting in smaller lags of transitions of the net heat flux and precipitation behind SST peaks.

Analysis of thermal conductivity of porous concrete using laboratory measurements and microstructure models

Porous concrete pavement has been used to alleviate urban heat island (UHI) effect in the near-surface temperature field. Due to high porosity and heterogeneity, thermal properties of porous concrete may vary depending on the microstructure of aggregates and air voids. This paper developed an innovative method to calculate the effective thermal conductivity of porous concrete considering two-dimensional (2-D) heterogeneous microstructure. An image-aided approach was used to randomly generate the three-phase (aggregates, cement paste, and air) microstructure model of porous concrete. Finite-element (FE) models were developed to calculate thermal conductivity of porous concrete by simulating the steady heat transfer process. Laboratory experiments were conducted to measure thermal conductivity of porous concrete and its components. The prediction results were compared to experimental measurements and close agreements without statistical differences were observed. The effects of microstructure feature on thermal conductivity of porous concrete were studied. Although the thermal conductivity of porous concrete decreases as the porosity increases in general, the variation of thermal conductivity is affected by the tortuosity and orientation of air void and aggregate contacts. The study findings clearly indicate that the heterogeneous microstructure of porous concrete need be considered for accurate prediction of thermal transport behavior.

Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator

Exploring the effects of climate change on the hydrological response at the local scale requires climate data at high spatial and temporal resolutions. This is best achieved by generating downscaled ensembles of future climate variables derived from climate models. For this purpose we present a methodology to re-parameterize the AWE-GEN-2d model (Advanced WEather GENerator for a two-dimensional grid). The model simulates key meteorological variables needed by hydrological models and is particularly suitable to explore the effects of stochastic (natural) climatic uncertainty, which is fundamental for hydrological applications, especially at sub-kilometer and hourly scales. Factors of change for different climate statistics are calculated from climate model simulations of present and future climates and subsequently applied to the statistics derived from observations to re-parameterize AWE-GEN-2d. The model abilities in generating an ensemble of future climate variables for the transient period 2020–2089 is presented with examples of precipitation and near-surface air temperature fields from hourly to multi-annual scales for a small mountainous region in the Swiss Alps. The stochastic uncertainty is examined for present and future periods and for spatial scales from the RCM scale (12-km, daily) to 2-km demonstrating the potential use of AWE-GEN-2d outputs. At the RCM scale, model results yield a small increase in annual precipitation (4%) which is within the stochastic uncertainty range for present and future periods (7%). At the fine scale of 2-km, the increase in annual precipitation can exceed the stochastic uncertainty, but for less than 10% of the domain area. On the contrary, changes in annual near-surface air temperature exceed stochastic uncertainty both at the RCM and finer scales. Stochastic climate uncertainty was concluded to be very similar when comparing present and future periods and 12-km and 2-km scales. The benefits of using AWE-GEN-2d in hydrological climate change impact assessments are finally discussed.

On the Use of Rotary-Wing Aircraft to Sample Near-Surface Thermodynamic Fields: Results from Recent Field Campaigns.

Rotary-wing small unmanned aircraft systems (sUAS) are increasingly being used for sampling thermodynamic and chemical properties of the Earth’s atmospheric boundary layer (ABL) because of their ability to measure at high spatial and temporal resolutions. Therefore, they have the potential to be used for long-term quasi-continuous monitoring of the ABL, which is critical for improving ABL parameterizations and improving numerical weather prediction (NWP) models through data assimilation. Before rotary-wing aircraft can be used for these purposes, however, their performance and the sensors used therein must be adequately characterized. In the present study, we describe recent calibration and validation procedures for thermodynamic sensors used on two rotary-wing aircraft: A DJI S-1000 and MD4-1000. These evaluations indicated a high level of confidence in the on-board measurements. We then used these measurements to characterize the spatiotemporal variability of near-surface (up to 300-m AGL) temperature and moisture fields as a component of two recent field campaigns: The Verification of the Origins of Rotation in Tornadoes Experiment in the Southeast U.S. (VORTEX-SE) in Alabama, and the Land Atmosphere Feedback Experiment (LAFE) in northern Oklahoma.

How Are Turbulent Sensible Heat Fluxes and Snow Melt Rates Affected by a Changing Snow Cover Fraction?

The complex interaction between the atmospheric boundary layer and the heterogeneous land surface is typically not resolved in numerical models approximating the turbulent heat exchange processes. In this study, we consider the effect of the land surface heterogeneity on the spatial variability of near-surface air temperature fields and on snow melt processes. For this purpose we calculated turbulent sensible heat fluxes and daily snow depth depletion rates with the physics-based surface process model Alpine3D. To account for the effect of a heterogeneous land surface (such as patchy snow covers) on the local energy balance over snow, Alpine3D is driven by two-dimensional atmospheric fields of air temperature and wind velocity, generated with the non-hydrostatic atmospheric model ARPS. The atmospheric model is initialized with a set of snow distributions (snow cover fraction and number of snow patches) and atmospheric conditions (wind velocities) for an idealized flat test site. Numerical results show that the feedback of the heterogeneity of the land surface (snow, no snow) on the near-surface variability of the atmospheric fields result in a significant increase in the mean air temperature ∆TA= 1.8 K (3.7 K, 4.9 K) as the snow cover fraction is decreased from a continuous snow cover to 55 % (25 %, 5 %). Surface turbulent sensible heat fluxes and daily snow depth depletion rates are strongly correlated to mean air temperatures, leading to 22-40 % larger daily snow depth depletion rates for patchy snow covers. Mean air temperatures over snow heavily increase with increasing initial wind velocities and weakly increase with an increasing number of snow patches. Numerical results from the idealized test site are compared with a test site in complex terrain. As slope-induced atmospheric processes (such as the development of katabatic flows) modify turbulent sensible heat fluxes, the variation of the surface energy balance is larger in complex terrain than for an idealized flat test site.

Land use and topography influence in a complex terrain area: A high resolution mesoscale modelling study over the Eastern Pyrenees using the WRF model

Different types of land use (LU) have different physical properties which can change local energy balance and hence vertical fluxes of moisture, heat and momentum. This in turn leads to changes in near-surface temperature and moisture fields. Simulating atmospheric flow over complex terrain requires accurate local-scale energy balance and therefore model grid spacing must be sufficient to represent both topography and land-use. In this study we use both the Corine Land Cover (CLC) and United States Geological Survey (USGS) land use databases for use with the Weather Research and Forecasting (WRF) model and evaluate the importance of both land-use classification and horizontal resolution in contributing to successful modelling of surface temperatures and humidities observed from a network of 39 sensors over a 9day period in summer 2013. We examine case studies of the effects of thermal inertia and soil moisture availability at individual locations. The scale at which the LU classification is observed influences the success of the model in reproducing observed patterns of temperature and moisture. Statistical validation of model output demonstrates model sensitivity to both the choice of LU database used and the horizontal resolution. In general, results show that on average, by a) using CLC instead of USGS and/or b) increasing horizontal resolution, model performance is improved. We also show that the sensitivity to these changes in the model performance shows a daily cycle.

Sensitivity of Convection-Allowing Forecasts to Land Surface Model Perturbations and Implications for Ensemble Design

Abstract In this exploratory study, a series of perturbations to the land surface model (LSM) component of the Weather Research and Forecasting (WRF) Model was developed to investigate the sensitivity of forecasts of severe thunderstorms and heavy precipitation at 4-km grid spacing and whether such perturbations could improve ensemble forecasts at this scale. The perturbations (generated using a combination of perturbing fixed parameters and using separate schemes, one of which—Noah-MP—is new among the WRF modeling community) were applied to a 10-member ensemble including other mixed physics parameterizations and compared against an identically configured ensemble that did not include the LSM perturbations to determine their impact on probabilistic forecasts. A third ensemble using only the LSM perturbations was also configured. The results from 14 (in total) 36-h ensemble forecasts suggested the LSM perturbations resulted in systematic improvement in ensemble dispersion and error characteristics. Lower-tropospheric temperature, moisture, and wind fields were all improved, as were probabilistic precipitation forecasts. Biases were not systematically altered, although some outlier members are present. Examination of near-surface temperature and mixing ratio fields, surface energy fluxes, and soil fields revealed tendencies caused by certain perturbations. A case study featuring tornadic supercells illustrated the physical causes of some of these tendencies. The results of this study suggest LSM perturbations can sample a dimension of model error not yet sampled systematically in most ensembles and should be included in convection-allowing ensembles.

Evaluation of Thermosalinograph and VIIRS Data for the Characterization of Near-Surface Temperature Fields

AbstractDetailed understanding of submesoscale processes and their role in global ocean circulation is constrained, in part, by the lack of global observational datasets of sufficiently high resolution. Here, the potential of thermosalinograph (TSG) and Visible Infrared Imager Radiometer Suite (VIIRS) data is evaluated, to characterize the submesoscale structure of the near-surface temperature fields in the Gulf Stream and Sargasso Sea. In addition to spectral density, the structure function is considered, a statistical measure less susceptible to data gaps, which are common in the satellite-derived fields. The structure function is found to be an unreliable estimator, especially for steep spectral slopes, nominally between 2 and 3, typical of the Gulf Stream and Sargasso regions. A quality-control threshold is developed based on the number and size of gaps to ensure reliable spectral density estimates. Analysis of the impact of gaps in the VIIRS data on the spectra shows that both the number of missing values and the size of gaps affect the results, and that the steeper the spectral slope the more significant the impact. Furthermore, the TSG, with a nominal resolution of 75 m, captures the spectral characteristics of the fields in both regions down to scales substantially smaller than 1 km, while the VIIRS fields, with a nominal resolution of 750 m, reproduce the spectra well down to scales of about 20 km in the Sargasso Sea and 5 km in the Gulf Stream. The scales at which the VIIRS and TSG spectra diverge are thought to be determined by sensor and retrieval noise.

Downscaling near-surface atmospheric fields with multi-objective Genetic Programming

We present a new Genetic Programming based method to derive downscaling rules (i.e., functions or short programs) generating realistic high-resolution fields of atmospheric state variables near the surface given coarser-scale atmospheric information and high-resolution information on land surface properties. Such downscaling rules can be applied in coupled subsurface-land surface-atmosphere simulations or to generate high-resolution atmospheric input data for offline applications of land surface and subsurface models. Multiple features of the high-resolution fields, such as the spatial distribution of subgrid-scale variance, serve as objectives. The downscaling rules take an interpretable form and contain on average about 5 mathematical operations. The method is applied to downscale 10 m-temperature fields from 2.8 km to 400 m grid resolution. A large part of the spatial variability is reproduced, also in stable nighttime situations, which generate very heterogeneous near-surface temperature fields in regions with distinct topography.

The role of geometry changes and debris formation associated with wear on the temperature field in fretting contacts

The temperature of a fretting contact is known to be a key factor in its development. However, as a test proceeds, the wear scar changes, both geometrically and through the formation of oxide-based debris-beds. Accordingly, the effects of these on the near-surface temperature field resulting from frictional heating in fretting has been analysed via numerical modelling. Under the test conditions examined, it was predicted that (i) the development of the wear scar geometry would result in a significant (up to ~ 25%) reduction in the mean-surface temperature rise, and (ii) the formation of a typical oxide debris bed would result in a significant (up to ~ 80%) increase in the mean-surface temperature rise.

The feasibility of unmanned aerial vehicle-based acoustic atmospheric tomography.

A technique for remotely monitoring the near-surface air temperature and wind fields up to altitudes of 1 km is presented and examined. The technique proposes the measurement of sound spectra emitted by the engine of a small unmanned aerial vehicle using sensors located on the aircraft and the ground. By relating projected and observed Doppler shifts in frequency and converting them into effective sound speed values, two- and three-dimensional spatially varying atmospheric temperature and wind velocity fields may be reconstructed using tomography. The feasibility and usefulness of the technique relative to existing unmanned aerial vehicle-based meteorological techniques using simulation and trials is examined.

Assimilation of surface AWS using 3DVAR and LAPS and their effects on short-term high-resolution weather forecasts

The progress in data assimilation techniques that incorporate weather observations into high-resolution numerical weather prediction models is challenging because of handling surface data in terrain misrepresentation, balance approximations, instrument errors and sensor representativeness. In the framework of operational numerical weather prediction, two data assimilation systems are compared using conventional observations from surface Automatic Weather Stations (AWS), a three-dimensional variational analysis (3DVAR) and the Local Analysis and Prediction System (LAPS). The goal is to study the ability of these two systems to assimilate data from AWS and to assess which performs better for near-surface wind and temperature fields to initialize a short-range 1-km resolution forecast with the Weather Research and Forecasting (WRF) model. Results show that the 3DVAR assimilation patterns are unrealistic given the inhomogeneous nature of the near-surface fields in complex terrains. In contrast, LAPS analyses show a heterogeneous assimilation pattern, more consistent with the complexity of the terrain and the observations. During the model spin-up period, simulations initialized using both data assimilation methods approach rapidly the control simulation, initialized without assimilation. However, the 1km resolution forecasts initialized with LAPS exhibit a significant improvement, particularly for the wind field module.

Using fuzzified regression trees for statistical downscaling and regionalization of near surface temperatures in complex terrain

High mountain regions are characterized by a large climatic heterogeneity which is not sufficiently represented by state-of-the-art climate models or reanalysis products. With regard to the increasing demand for high-resolution temperature data for climate impact studies, a statistical approach is presented, which allows estimating high-resolution near-surface temperature fields in complex terrain. High-resolution free air temperatures are derived from climate model data by considering the current stratification of the atmosphere. The residuals compared with in situ observation of near-surface temperatures are subsequently analyzed using a regression tree approach with suitable large-scale atmospheric and local-scale terrain parameters as predictors. The model identifies the predominant synoptic and topographic controls for the local-scale distribution of residuals and can be used to regionalize residual fields with high spatial resolution. The disadvantage that a tree-structured model generates stepwise constant predictant values can be overcome by integrating a fuzzifying routine. A fuzzified regression tree model was applied to analyze and predict the spatial and temporal variability of topographically induced temperatures for a target area in the Central Himalayas. Large-scale atmospheric variables, derived from the ERA-Interim reanalysis, and local terrain parameters were used as potential predictors. The model sufficiently identified the main influencing factors for the temperature heterogeneity. The potential solar insolation was found to be the predominant predictor, but also, hydroclimatic large-scale variables were found to be crucial. During clear nights, the model showed a distinct elevation dependency of residuals which indicates the importance of nocturnal cold air drainage and accumulation for the local-scale temperature distribution in the highly structured target area.