Longwave Radiation Research Articles

Clouds reduce downwelling longwave radiation over land in a warming climate.

Clouds greatly influence the Earth's energy balance1,2. Observationally constraining cloud radiative feedback, a notably uncertain climate feedback mechanism3-5, is crucial for improving predictions of climate change5-7 but, so far, remains an elusive objective, and the feedback may be different over the ocean versus over land8-10. Here we show a local negative surface longwave cloud feedback over land at the southern Great Plains site, constrained by direct long-term observation of spectrally resolved downwelling longwave radiance11. This negative cloud feedback at thesouthern Great Plains site causes a -1.77 ± 1.15 W m-2 per decade change in downwelling longwave radiation and suggests that cloud changes may partially modulate the warming effect of increased greenhouse gas concentrations and atmospheric temperatures over land. Specifically, our results are derived from an optimal spectral fingerprinting method12-15 designed to separate surface longwave cloud feedback from other surface forcings and feedbacks, by making use of their unique spectral signatures13-18 in the long-term record of spectrally resolved radiances. Furthermore, we show that the results are not sitespecific: negative surface longwave cloud feedbacks, primarily induced by decreasing low cloud cover in warming climates, are commonly observed over land in reanalysis and satellite datasets. Our findings establish a pivotal observational benchmark of radiative forcing and feedback needed for validating climate model performance over land.

Implementing an Outgoing Longwave Radiation Climate Dataset from Fengyun 3E Satellite Data with a Machine-Learning Algorithm

China’s FengYun 3E (FY3E) meteorological satellite, launched in 2021, is equipped with advanced instruments for comprehensive Earth observations. In this study, we compared outgoing longwave radiation (OLR) measurements from the FY3E satellite (FY3E OLR) and from a series of satellites operated by the National Oceanic and Atmospheric Agency (NOAA, United States of America; hereafter NOAA OLR) and analyzed the spatiotemporal differences between the datasets. We designed a new correction model, “DeepFM”, implementing both a factorization machine algorithm and a deep artificial neural network to minimize daily mean differences between the datasets. Then, we evaluated the spatiotemporal consistency between the corrected FY3E OLR and NOAA OLR data. The DeepFM model effectively reduced daily mean differences: after correction, the daily mean absolute bias and root-mean-square error decreased from 7.4 W/m2 to 4.2 W/m2 and from 10.3 W/m2 to 6.3 W/m2, respectively, indicating a notably improved spatiotemporal consistency between the corrected FY3E OLR and NOAA OLR data. Subsequently, we merged these datasets to generate a long-term OLR dataset suitable for climate analyses. This study provides a robust technological basis and innovative methodology for the dedicated application of China meteorological satellites to climate science.

Observed determinants of urban outdoor thermal exposure during hot summer and snowy winter periods in a humid continental climate

Many cities in the midlatitudes experience both extreme heat and cold, and pedestrians are exposed to thermal extremes that cause bodily stress. With growing urban populations, city design that contributes to mitigating summer heat while reducing winter cold exposure is increasingly important. Pedestrian thermal exposure depends on several microclimatic factors, including shortwave and longwave radiation absorption, which can be quantified by the mean radiant temperature (Tmrt). Limited research has been conducted on the radiative components of thermal exposure in hot, humid summers and cold, snowy winters. We gathered micrometeorological data from diverse urban sites and in multiple seasons in Guelph, Canada, using a mobile human-biometeorological weather station (MaRTy cart) that applies the six-directional method to determine Tmrt. Seasonal datasets were analysed and compared to examine the drivers of thermal exposure and recommend strategies for mitigating heat and cold stress. In summer, shade is the primary factor that reduces daytime heat exposure and it slightly increases nighttime heat exposure. Enhanced pervious ground cover is a secondary factor day and night. In winter, reduced shade alleviated daytime cold exposure, while snow cover provided daytime benefits from increased solar reflections and post-sunset penalties associated with reduced longwave radiation from low snow surface temperatures.

Energy Meteorology for the Evaluation of Solar Farm Thermal Impacts on Desert Habitats

This work addresses challenges and opportunities in the evaluation of solar power plant impacts, with a particular focus on thermal effects of solar plants on the environment and vice-versa. Large-scale solar power plants are often sited in arid or desert habitats, which tend to include fauna and flora that are highly sensitive to changes in temperature and humidity. Our understanding of both shortwave (solar) and longwave (terrestrial) radiation processes in solar power plants is complete enough to render the modeling of radiation fluxes with high confidence for most applications. In contrast to radiation, the convective environment in large-scale solar power plants is much more difficult to characterize. Wind direction, wind speed, turbulence intensity, dust concentration, ground condition, panel configuration density, orientation and distribution throughout the solar field, all affect the local environment, the balance between radiation and convection, and in turn, the performance and thermal impact of solar power plants. Because the temperatures of the two sides of photovoltaic (PV) panels depend on detailed convection–radiation balances, the uncertainty associated with convection affects the heat and mass transfer balances as well. Those balances are critically important in estimating the thermal impact of large-scale solar farms on local habitats. Here we discuss outstanding issues related with these transfer processes for utility-scale solar generation and highlight potential pathways to gain useful knowledge about the convective environment directly from solar farms under operating conditions.

НЕКОТОРЫЕ ВОПРОСЫ О ТЕПЛООБЕСПЕЧЕННОСТИ В СИСТЕМЕ «ПОЧВА - РАСТЕНИЕ - ВОЗДУХ»

During the growing season, cultivated plants are subject to bifurcation effects such as drought, frost, ice, hail, heavy rains, etc. The dependence of mankind on the weather is quite large. The purpose of this work is to study artificial bifurcation by heat supply of plants in laboratory conditions. The thermophysical characteristics of the interaction of the plant-soil-air system are due to radiation heat exchange, which includes the arrival of long-wave radiation from the atmosphere - plants and reflected radiation from plants into the atmosphere; the arrival of short-wave radiation from solar radiation used by plant leaves for photosynthesis and reflection of part of this photoactive radiation; turbulent heat exchange of the soil surface with the surrounding surface air; transpiration heat exchange of the soil surface with the surrounding surface air; heat exchange in the soil. In addition, solar radiation reaching the slope surface has a different scattering effect on the functioning of the plant-soil-air system. Note that the vegetation cover performs the function of thermal insulation between the atmosphere and the soil. The analytical determination of the heat supply of plants is quite difficult, especially on slopes of different steepness and exposure, due to both the insufficiency of the data included in these analytical expressions, as well as the uncertainty (impossibility) of determining experimentally appropriate coefficients. Therefore, the technique of the bifurcation approach allows, as a first approximation, to design certain technologies for cultivating crops for a specific catchment area, both in terms of frost resistance, and to apply measures to protect against frost. The stability of plant seedlings to frost was checked in laboratory conditions: sowing and germination of barley seeds under the same conditions (according to ambient temperature and soil moisture) for seven days; placing in a refrigerator at -18°C; extraction from the refrigerator at intervals of 5 minutes, maximum duration of 50 minutes; visual monitoring of the condition of plants. Experimental studies have shown that the death of a plant from frost is described by the regression equation y = 0,0268x2 – 0,3701x + 17,232. As a result of the research, a bifurcation function has been proposed, confirmed by laboratory experiments.

A Numerical Study of the Impact of Topography on the July 2021 Extreme Rainfall Event in Zhengzhou, China

AbstractAn unprecedented extreme rainfall event occurred in Zhengzhou, Henan Province, China, in July 2021. To understand the impact of local topography on this extreme rainfall event, the Weather Research and Forecasting model is configured with 27 and 9 km model grid spacings (MGS), along with United States Geological Survey (USGS) topography data at 8.3 and 0.9 km resolutions, called MGS27_USGS8.3, MGS9_USGS8.3, and MGS9_USGS0.9. Results show that the 9 km MGS, permitting activities with coarse γ scales (∼20 km), successfully reproduces the generation of mesoscale cyclones. However, the finer topography enables a more accurate representation of orographic blocking and lifting effects, thereby adjusting the position of the mesoscale cyclone. It can depict the location of adiabatic processes, local circulation, and vertical pressure gradient forces more accurately, thereby adjusting the position of topography‐induced vertical motions. The turbulence diagnostics show that the topography‐induced lifting motion enhances clouds that block longwave radiation, leading to local environment warming, which in turn enhances turbulence and further amplifies the updrafts, ultimately improving the spatial distribution and temporal variation of precipitation. This study provides insights for an in‐depth understanding of the mechanisms of topography on extreme rainfall.

Atmospheric rivers cause warm winters and extreme heat events.

Atmospheric rivers (ARs) are narrow regions of intense water vapour transport in the Earth's atmosphere. These transient phenomena carry water from the subtropics to the mid-latitudes and polar regions1,2, making up the majority of polewards moisture transport3-5 and exerting control on the precipitation and water resources in many regions6,7. In addition to transporting moisture, ARs also transport heat, but the impact of this transport on global near-surface air temperatures has not yet been characterized. Here we show that seasons with more frequent ARs also have warmer than average temperatures in many mid-latitude regions, and that AR events are associated with temperature anomalies of 5-10 °C above the climatological mean. This is due to anomalous horizontal transport and convergence of sensible heat and moisture in the lower atmosphere, which increases both downward sensible heat flux and downwelling long-wave radiation at the surface. On an hourly timescale, over 70% of extreme warm-temperature anomalies occur within ARs in large portions of the mid-latitudes, and ARs are associated with moist and compound heatwaves in many regions worldwide, suggesting that consideration of ARs may improve predictive capability for certain extreme heat events. Our results demonstrate that ARs significantly impact air temperatures on a wide array of timescales, and that they may play a wider role in global energy transport than previously recognized.

Satellite Signatures of Pre-Seismic Atmospheric Anomalies of February 6, 2023 Türkiye Earthquakes

Time series satellite data, coupled with available ground-based observations, enable geophysicists to survey earthquake precursors in areas of strong geotectonic activity. This paper is focused on pre-seismic atmospheric disturbances resulting from the stress accumulated during the seismogenic process related to the 6 February 2023 Kahramanmaras doublet earthquake sequence in Türkiye. We investigated the pre- and post-seismic anomalies of multiple precursors of different spatiotemporal patterns from MODIS Terra/Aqua and NOAA-AVHRR satellite data (air temperature at 2 m height—AT, air relative humidity—RH, and air pressure—AP, surface outgoing long-wave radiation—OLR, and land surface temperature—LST). Pre-seismic recorded anomalies of AT within seven months and OLR within one month before the main shocks suggested the existence of the preparatory process of the Kahramanmaras doublet earthquake. The 8-Day LST_Day and LST_night data evidenced pre-seismic and post-seismic thermal anomalies for both the Pazarcik and Elbistan earthquakes. The results of this study highlight that the spatiotemporal evolution of earthquake precursors can be important information for updating the seismic hazard in geotectonic active areas.

Evaluation of the Maximum Evaporation and the Priestley‐Taylor Models for Inland Waterbodies

AbstractThe Priestley‐Taylor (PT) model is a classic model of potential evaporation of terrestrial and marine surfaces. It is now recognized that the Bowen ratio (β) implicit in the PT model is too sensitive to temperature. The model also requires the surface net radiation (Rn) as input even though Rn is not an independent external forcing. The maximum evaporation model (MEM) proposed by Yang and Roderick (2019, https://doi.org/10.1002/qj.3481) is a potential candidate for replacing the PT model. Past studies have evaluated the MEM for land ecosystems and for the global ocean. This study represents the first evaluation of the MEM for inland waterbodies. Results are based on eddy‐covariance observation at a large lake and at a small fishpond. Although there were complex error structures and error compensation patterns among its intermediate variables, the MEM was able to reproduce the observed monthly (R2 > 0.95) and interannual variability (R2 > 0.78) in the lake latent heat flux. A key assumption of the MEM, that the incoming and outgoing longwave radiation is coupled, was a reasonable approximation at both the monthly and the annual time scale for the large lake and at the monthly time scale for the small fishpond. This assumption allows the MEM to treat Rn as an intermediate variable instead of a prescribed forcing. These results support the MEM as an alternative to the PT model at locations where Rn measurements are not available. In situations where Rn data is available, a revised PT model with reduced β temperature sensitivity is recommended.

Combined Influence of 10–30‐Day Tropical and Mid–High‐Latitude Intraseasonal Oscillations on the Rapid Increases of Humid Heatwaves in Southern China

AbstractAs a typical hotspot of heatwaves, southern China experienced a marked shift from dry to humid heatwaves in the recent decade of 2013–2022. Significant 10–30‐day intraseasonal oscillation (ISO) in temperature and humidity is a major contributor to the humid heatwaves. Configuration of the 10–30‐day quasi‐barotropic wave train at mid–high latitudes with the dipolar pattern of tropical convection leads to the concurrence of strong moisture convergence and anomalous descents over southern China. The increased downward longwave radiation in association with moisture enhancement, shortwave radiation and adiabatic heating in association with descents bring about the persisting hot and humid conditions in humid heatwaves. Due to stronger barotropic energy conversion from mean flow to 10–30‐day circulation at mid–high latitudes, and stronger tropical convection activities in the recent decade, the occurrence of 10–30‐day circulation configuration induced humid heatwaves has increased by 13.3%, thus leading to the predominance of humid heatwaves.

Role of QBO and MJO in Sudden Stratospheric Warmings: A Case Study

The impact of the quasi-biennial oscillation (QBO) and Madden–Julian oscillation (MJO) on the dynamics of major sudden stratospheric warmings (SSWs) observed in the winters of 2018, 2019, and 2021 is investigated. Using data from the MERRA-2 reanalysis, we analyze the daily mean variability of critical atmospheric parameters at the 10 hPa level, including zonal mean polar cap temperature, zonal mean zonal wind, and the amplitudes of planetary waves 1 and 2. The results reveal dramatic increases in polar cap temperature and significant wind reversals during the SSW events, particularly in 2018. The analysis of planetary wave (PW) amplitudes demonstrates intensified wave activity coinciding with the onset of SSWs, underscoring the pivotal role of PWs in these stratospheric disruptions. Further examination of outgoing long-wave radiation (OLR) anomalies highlights the influence of QBO phases on tropical convection patterns. During westerly QBO (w-QBO) phases, enhanced convective activity is observed in the western Pacific, whereas the easterly QBO (e-QBO) phase shifts convection patterns to the maritime continent and central Pacific. This modulation by QBO phases influences the MJO’s role during SSWs, affecting tropical and extra-tropical weather patterns. The day-altitude variability of upward heat flux reveals distinct spatiotemporal patterns, with pronounced warming in the polar regions and mixed heat flux patterns in low latitudes. The differences observed between the SSWs of 2017–2018 and 2018–2019 are likely related to the varying QBO phases, emphasizing the complexity of heat flux dynamics during these events. The northern annular mode (NAM) index analysis shows varied responses to SSWs, with stronger negative anomalies observed during the e-QBO phase compared to the w-QBO phases. This variability highlights the significant role of the QBO in shaping the stratospheric and tropospheric responses to SSWs, impacting surface weather patterns and the persistence of stratospheric anomalies. Overall, the study demonstrates the intricate interactions between stratospheric dynamics, QBO, and MJO during major SSW events, providing insights into the broader implications of these atmospheric phenomena on global weather patterns.

Land-use induced changes in extreme temperature predominantly influenced by downward longwave radiation

Land use is key in regulating surface temperature, yet these relationships are often obscured by long-term mean responses. Here we employed numerical multi-model results to investigate the response of the surface temperature to land use change, especially its lower tails corresponding to boreal winter. The surface temperature decrease in the lower tails can exhibit up to ten times greater than the mean response to land use change over both the historical and future periods. Downward longwave radiation has emerged as the most remarkable contributing factor in controlling surface temperature change in mid-high latitudes of the Northern Hemisphere. Land use change can modify surface energy balance through land-atmosphere firstly, thereby regulate spatial patterns of water vapor and cloud cover in the Northern Hemisphere through teleconnection. The unity of local and remote effects influences the levels of downward longwave radiation and altering surface temperature at mid-high latitudes in extreme cold seasons.

Sensitivity Analysis and Validation of WRF-ARW Parameterizations for Potential Solar and Wind Energy Sources over Malaysia

Abstract This study investigates how accurately the Weather Research and Forecasting (WRF) simulation anticipates the viability of renewable energy options in Malaysia, with specific attention paid to wind and solar power potential. Due to Malaysia’s transition toward sustainable energy, precise forecasting is essential for grid stability and efficient integration. The research used simulations across East and West Malaysia for October 2022 with varying parameterization schemes to determine the most suitable configurations for accurate energy predictions. Eleven unique scheme permutations were analyzed against Mesonet, ERA5-Land, and Centre for Marine and Coastal Studies (CEMACS) datasets. The results revealed that certain permutations, specifically run 9 and run 10, significantly reduced the root-mean-square error (RMSE) for predicting radiation and wind speed outputs, indicating higher accuracy in simulations. The two best runs were then validated for November 2022 and used to estimate wind (run 10: WRF single-moment 6-class microphysics, RRTMG longwave radiation, Dudhia shortwave radiation, unified Noah land surface model, MYJ PBL, Eta surface layer, and new Tiedtke cumulus schemes) and solar photovoltaic (run 9: Morrison 2-moment microphysics, CAM longwave radiation, RRTMG shortwave radiation, unified Noah land surface model, MYJ PBL, and Eta surface layer schemes) potential maps over CEMACS and Kuching locations. Solar photovoltaic energy was estimated at a total of 3165 and 3379 kWh for CEMACS and Kuching, respectively, and a good potential overall, ranging from about 5500 to 7881 kWh, while wind energy had a much more variability appearance of 115 936 and 6250 kWh for CEMACS and Kuching, respectively. This study highlights the potential for targeted weather forecasting models to enhance the reliability and integration of renewable energy sources into national grids. Significance Statement This study explores the effective use of weather forecasting models to enhance renewable energy generation in Malaysia, a region increasingly focusing on sustainable energy solutions. By analyzing and validating various weather model configurations, this research provides vital information for selecting optimal settings for accurate predictions of solar and wind energy potential. This crucial advancement, which effectively integrates renewable energy into power grids to reliably provide a steady and efficient flow of energy, is essential for moving progressively closer to a more sustainable and eco-friendly energy. The results of this study both enhance scientific comprehension of atmospheric simulations utilized in energy sectors and carry meaningful consequences for policy formation and renewable infrastructure evolution within tropical regions.

Trends of Summer Lake Surface Water Temperature on the Tibetan Plateau and Their Response to Climate Change

AbstractThe Tibetan Plateau (TP) is covered by numerous lakes, and lake surface water temperature (LSWT) is an essential indicator of climate change, while few observations hinder our understanding of LSWT variation and its causes over TP. This study aims to simulate the summer LSWT long‐term trends of 81 TP lakes during 1980–2018 and quantify the impacts and contributions of atmospheric variables. Results show that TP lakes warmed with 0.32°C decade−1 on average. Northern TP lakes warmed faster than the southern ones (0.44 vs. 0.16°C decade−1) due to stronger trends of atmospheric variables and higher sensitive of colder lakes to atmospheric changes. 55 (67.9%) lakes of the total lakes studied in current work warmed slower than air due to weakened shortwave radiation (SW↓). Attribution analysis suggests that the air warming and wetting over TP dominate lakes' warming. Regarding synthesis contributions, air warming contributed 79.3%, with increased surface air temperature (SAT) and downward longwave radiation (LW↓) accounting for 41.6% and 37.7%, respectively, and air wetting indicated by increased surface specific humidity (SSH) contributed 39.0%, followed by a positive contribution (16.8%) from declined wind speed (WS). The negative contribution (−35.1%) from weakened SW↓ nearly counterbalances the positive effects of increased LW↓. 55.1% of the total synthesis contribution arises from the cross contribution through interactions among atmospheric variables and is mainly reflected in SAT and SSH, accounting for 26.8% and 24.8%, respectively. The findings enhance understanding of climate change impacts on lake systems and offer insights for lake resource management.

Modification of IPI Method for Extraction of Short-Term and Imminent OLR Anomalies and Case Study of Two Large Earthquakes

The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which have demonstrated their capability to detect significant short-term and ionospheric anomalies preceding earthquake events. In this study, the IPI method was applied to infrared satellite observation data for the first time, and a new algorithm for extracting short-term and imminent anomalies from infrared earthquakes was explored based on the IPI method, from which we obtained the MIPI (Modified Improved Pattern Informatics Method). Using 1° × 1° nighttime Outgoing Longwave Radiation (OLR) data from NOAA_18 satellites of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC) of the United States, the evolution of OLR anomalies before the Ridgecrest Ms 6.9 earthquake in the United States on 6 July 2019 as recorded by the China Earthquake Networks Center (CENC) and the Maduo Ms 7.4 earthquake in China on 21 May 2021 as recorded by the China Earthquake Networks Center (CENC) were studied. In order to make the IPI method suitable for the calculation of OLR data, two modifications were made to the IPI algorithm: (1) the quartile method was applied for automatically determining the abnormal changes in the OLR observation data and they were used as the input data instead of ionospheric data; (2) the standard deviation of the multi-year OLR residual data of each grid was used instead of the maximum anomaly index used in the original method to re-assign and obtain the relative anomaly index, and finally the anomaly evolution time series diagram was drawn. The results show the following: (1) The MIPI method can effectively extract short-term and imminent OLR anomalies prior to earthquakes. (2) Short-term and imminent OLR anomalies appeared about two weeks before each earthquake and lasted until the earthquake occurrence, disappearing after the earthquake. During this process, the anomalies exhibited a certain evolutionary trend. (3) The short-term and imminent OLR anomalies prior to each earthquake were distributed near the epicenter or near the seismogenic fault, about 200 KM away from the epicenters. The above results are similar to the spatiotemporal evolution characteristics of seismic infrared short-term anomalies previously studied, which indicates that the MIPI method can effectively extract seismic infrared anomalies and might provide a practical method for the extraction of seismic infrared short-term and imminent anomalies.

Role of Dry Dynamics in the Maritime Continent Barrier Effect in the Madden–Julian Oscillation

Abstract Eastward-moving moist deep convection and atmospheric circulation signals associated with the tropical Madden–Julian oscillation (MJO) sometimes break down as they cross the Maritime Continent region, but other times, the signal propagates across the region maintaining amplitude or regaining it over the west Pacific basin. This paper assesses the hypothesis that upper-tropospheric zonal diffluence of the background wind over the Maritime Continent causes much of this Maritime Continent barrier effect and its variation over time, through two mechanisms: 1) by slowing down the MJO as stronger-than-average background upper-tropospheric zonal wind over the Indian Ocean advects the MJO circulation signal westward, slowing its eastward advance, and 2) through the zonal advection of the background wind by subseasonal zonal wind across a region of zonal diffluence of the background wind, which advects the background wind of the opposite sign to the MJO wind. Advection of the opposite-signed background wind counteracts the MJO wind and reduces its associated upper-tropospheric mass divergence, weakening the mechanisms of the upper-tropospheric Kelvin wave component of the MJO circulation. Composites of MJO-associated zonal wind and outgoing longwave radiation signals diminish as they cross the Maritime Continent region when the region’s background zonal winds are diffluent, and composites of data reconstructing the relevant advection terms reveal the direct action of the advection mechanisms. Significance Statement The Madden–Julian oscillation (MJO) is the leading subseasonal variation of the tropical atmosphere. This project addresses how diffluence of the upper-tropospheric background zonal wind can break down MJO events through advection of and by the background wind.

PM2.5 reduces the daytime/nighttime urban heat island intensity over mainland China

PM2.5 pollution severely threatens people's living environment and health, significantly affect urban heat island (UHI). In this study, in-situ observed data were combined with satellite data via refined pollution intensity classification methods, and the relationships between PM2.5 and canopy/surface UHI (CUHI/SUHI) were analyzed. PM2.5 reduced the UHI in nearly all the environments, with more pronounced effects during clear-sky daytime and winter (-0.33 °C for the SUHI) and less pronounced effects at all-sky nights and in summer. For the former, PM2.5 primarily diminishes the UHI intensity (UHII) by weakening incident radiation; this radiative forcing effect is exacerbated by the combination of PM2.5 and high humidity, which is further amplified by winter pollution peaks. At night, the UHII primarily influenced by the initial temperature conditions during the day. Less intake of surface energy on high-pollution days, which in turn affects longwave radiation from the surface/canopy at night. Additionally, PM2.5 has been found to significantly influence UHI through its interaction with potential influential factors and its filtering effect (different PM2.5 levels correspond to varying conditions of these factors). This study with new insights into the impact of PM2.5 on UHI could aid in the design of strategies to improve urban heat and pollution environments.

Sky Temperature Forecasting in Djibouti: An Integrated Approach Using Measured Climate Data and Artificial Neural Networks

Buildings exchange heat with different environmental elements: the sun, the outside air, the sky, and outside surfaces (including the walls of environmental buildings and the ground). To correctly account for building energy performance, radiative cooling potential, and other technical considerations, it is essential to evaluate sky temperature. It is an important parameter for the weather files used by energy building simulation software for calculating the longwave radiation heat exchange between exterior surfaces and the sky. In the literature, there are several models to estimate sky temperature. However, these models have not been completely satisfactory for the hot and humid climate in which the sky temperature remains overestimated. The purpose of this paper is to provide a comprehensive analysis of the sky temperature measurement conducted, for the first time, in Djibouti, with a pyrgeometer, a tool designed to measure longwave radiation as a component of thermal radiation, and an artificial neural network (ANN) model for improved sky temperature forecasting. A systematic comparison of known correlations for sky temperature estimation under various climatic conditions revealed their limited accuracy in the region, as indicated by low R2 values and root mean square errors (RMSEs). To address these limitations, an ANN model was trained, validated, and tested on the collected data to capture complex patterns and relationships in the data. The ANN model demonstrated superior performance over existing empirical correlations, providing more accurate and reliable sky temperature predictions for Djibouti’s hot and humid climate. This study showcases the effectiveness of an integrated approach using pyrgeometer-based sky temperature measurements and advanced machine learning techniques ANNs for sky temperature forecasting in Djibouti to overcome the limitations of existing correlations and improve the accuracy of sky temperature predictions, particularly in hot and humid climates.

A More Transparent Infrared Window

AbstractThe infrared window region (780–1,250 cm−1, 12.8 to 8.0 μm) is of great importance to Earth's climate due to its high transparency and thermal energy. We present here a new investigation of the transparency of this spectral region based on observations by interferometers of downwelling surface radiance at two DOE Atmospheric Radiation Measurement program sites. We focus on the dominant source of absorption in this region, the water vapor continuum, and derive updated values of spectral absorption coefficients for both the self and foreign continua. Our results show that the self continuum is too strong in the previous version of Mlawer‐Tobin_Clough‐Kneizys‐Davies (MT_CKD) water vapor continuum model, a result that is consistent with other recent analyses, while the foreign continuum is too weak in MT_CKD. In general, the weaker self continuum derived in this study results in an overall increase in atmospheric transparency in the window, although in atmospheres with low amounts of water vapor the transparency may slightly decrease due to the increase in foreign continuum absorption. These continuum changes lead to a significant decrease in downwelling longwave flux at the surface for moist atmospheres and a modest increase in outgoing longwave radiation. The increased fraction of surface‐leaving radiation that escapes to space leads to a notable increase (∼5–10%) in climate feedback, implying that climate simulations that use the new infrared window continuum will show somewhat less warming than before. This study also points out the possibly important role that aerosol absorption may play in the longwave radiative budget.

Optimal Configuration of Physical Process Parameterization Scheme Combination for Simulating Meteorological Variables in Weather Research and Forecasting Model: Based on Orthogonal Experimental Design and Comprehensive Evaluation Method

The weather research and forecasting (WRF) model is frequently used to investigate the meteorological field around nuclear installations. The configuration of physical process parameterization schemes in the WRF model has a significant impact on the accuracy of the simulation results. Consequently, carrying out a pre-experiment to quickly obtain the optimal combination of parameterization schemes is essential before conducting meteorological parameter research. To obtain the optimal combination of physical process parameterization schemes from the planetary boundary layer (PBL), land surface (LSF), microphysical (MP), long-wave (LW), and short-wave (SW) radiation processes of the WRF model for simulating the near-surface meteorological variables near a nuclear power plant in Sanshan Town, Fuqing City, Fujian Province, China on 4 June 2019 were observed. Orthogonal experimental design (OED), a comprehensive evaluation method based on the CRiteria Import Through Intercriteria Correlation (CRITIC) weight analysis, and comprehensive balance method were employed for the first time to conduct the research. The sensitivity of meteorological variables to physical processes was first discussed. The findings revealed that the PBL scheme configuration had a profound impact on simulating wind fields. Furthermore, the LSF scheme configuration had a significant influence on simulating near-surface temperature and relative humidity, which was much greater than that of other physical processes. In addition, the choice of the radiation scheme had a significant impact on how the temperature was distributed close to the ground and how the wind field was simulated. Furthermore, the configuration of the MP scheme was found to exert a certain influence on the simulation of relative humidity; however, it demonstrated a weak influence on other meteorological variables. Secondly, The MYNN3 scheme for PBL process, the NoahMP scheme for LSF process, the WSM5 scheme for MP process, the RRTMG scheme for LW process, and the Dudhia scheme for SW process are found to be the comprehensive optimal physical process parameterization scheme combination for simulating meteorological variables in the research area selected in this study. As evident from the findings, the use of the OED method to obtain the combinations of the optimal physical process parameterization scheme could successfully reproduce the wind field, temperature, and relative humidity in the current study. Thus, this method appears to be highly reliable and effective for use in the WRF models to explore the optimal combinations of the physical process parameterization scheme, which could provide theoretical support to quickly analyzing accurate meteorological field data for longer periods and contribute to deeply investigating the migration and diffusion behavior of airborne pollutants in the atmosphere.