Hasil untuk "Meteorology. Climatology"

G. Jendritzky, R. Dear, G. Havenith

J. Fisher, K. Tu, D. Baldocchi

I. Auer, R. Böhm, A. Jurković et al.

K. Sperber, H. Annamalai, I. Kang et al.

M. A. White, P. Thornton, S. Running

D. Adams, A. Comrie

L. Horowitz, S. Walters, D. Mauzerall et al.

Philip H. Ramsey

C. Kidd, Andreas Becker, G. Huffman et al.

J. Vidal, E. Martin, L. Franchistéguy et al.

F. Isotta, C. Frei, Viktor Weilguni et al.

S. Vannitsem, J. Bremnes, J. Demaeyer et al.

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space–time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed.

A. Butler, D. Seidel, S. Hardiman et al.

M. T. Chaudhary, A. Piracha

Natural hazards are processes that serve as triggers for natural disasters. Natural hazards can be classified into six categories. Geophysical or geological hazards relate to movement in solid earth. Their examples include earthquakes and volcanic activity. Hydrological hazards relate to the movement of water and include floods, landslides, and wave action. Meteorological hazards are storms, extreme temperatures, and fog. Climatological hazards are increasingly related to climate change and include droughts and wildfires. Biological hazards are caused by exposure to living organisms and/or their toxic substances. The COVID-19 virus is an example of a biological hazard. Extraterrestrial hazards are caused by asteroids, meteoroids, and comets as they pass near earth or strike earth. In addition to local damage, they can change earth inter planetary conditions that can affect the Earth’s magnetosphere, ionosphere, and thermosphere. This entry presents an overview of origins, impacts, and management of natural disasters. It describes processes that have potential to cause natural disasters. It outlines a brief history of impacts of natural hazards on the human built environment and the common techniques adopted for natural disaster preparedness. It also lays out challenges in dealing with disasters caused by natural hazards and points to new directions in warding off the adverse impact of such disasters.

Soubhik Biswas, Andrew Dowdy, Savin Chand

Understanding how weather and climate influence fire risk is important for many purposes, including climate adaptation planning and decision-making in sectors such as emergency management, finance, health and infrastructure (e.g., for energy and water availability). In this study, bias-corrected 20CRv2c reanalysis data are used to investigate the climatology and long-term trends of weather conditions associated with landscape fires in Australia. The McArthur Forest Fire Danger Index (FFDI) is used here as a broad-scale representation of weather conditions known to influence fire behaviour based on wind speed, humidity, temperature and rainfall measures. In particular, using this reanalysis dataset allows analysis over a longer time period than previous studies, from 1876 to 2011. Another novel aspect is that trends are examined using several different approaches, including a method to help account for the influence of interannual drivers of climate variability not previously used for fire weather analysis. Results show increases in mean and extreme seasonal FFDI values throughout Australia in general, with all statistically significant trends being positive in sign for individual climate zones. Humidity and temperature trends, attributable to human-caused climate change, are shown to be the main cause of the increase in dangerous weather conditions for fires. These findings build on previous studies, with the novel data and methods used adding confidence to the overall understanding of fire risk factors in a changing climate.

Paulius Rauba, Viktor Cikojevic, Fran Bartolic et al.

Weather forecasts sit upstream of high-stakes decisions in domains such as grid operations, aviation, agriculture, and emergency response. Yet forecast users often face a difficult trade-off. Many decision-relevant targets are functionals of the atmospheric state variables, such as extrema, accumulations, and threshold exceedances, rather than state variables themselves. As a result, users must estimate these targets via post-processing, which can be suboptimal and can introduce structural bias. The core issue is that decisions depend on distributions over these functionals that the model is not trained to learn directly. In this work, we introduce GEM-2, a probabilistic transformer that jointly learns global atmospheric dynamics alongside a suite of variables that users directly act upon. Using this training recipe, we show that a lightweight (~275M params) and computationally efficient (~20-100x training speedup relative to state-of-the-art) transformer trained on the CRPS objective can directly outperform operational numerical weather prediction (NWP) models and be competitive with ML models that rely on expensive multi-step diffusion processes or require bespoke multi-stage fine-tuning strategies. We further demonstrate state-of-the-art economic value metrics under decision-theoretic evaluation, stable convergence to climatology at S2S and seasonal timescales, and a surprising insensitivity to many commonly assumed architectural and training design choices.

S. Vicente‐Serrano, T. McVicar, D. Miralles et al.

This review examines the role of the atmospheric evaporative demand (AED) in drought. AED is a complex concept and here we discuss possible AED definitions, the subsequent metrics to measure and estimate AED, and the different physical drivers that control it. The complex influence of AED on meteorological, environmental/agricultural and hydrological droughts is discussed, stressing the important spatial differences related to the climatological conditions. Likewise, AED influence on drought has implications regarding how different drought metrics consider AED in their attempts to quantify drought severity. Throughout the article, we assess literature findings with respect to: (a) recent drought trends and future projections; (b) the several uncertainties related to data availability; (c) the sensitivity of current drought metrics to AED; and (d) possible roles that both the radiative and physiological effects of increasing atmospheric CO2 concentrations may play as we progress into the future. All these issues preclude identifying a simple effect of the AED on drought severity. Rather it calls for different evaluations of drought impacts and trends under future climate scenarios, considering the complex feedbacks governing the climate system.

M. Krämer, C. Rolf, N. Spelten et al.

Abstract. This study presents airborne in situ and satellite remote sensing climatologies of cirrus clouds and humidity. The climatologies serve as a guide to the properties of cirrus clouds, with the new in situ database providing detailed insights into boreal midlatitudes and the tropics, while the satellite-borne data set offers a global overview. To this end, an extensive, quality-checked data archive, the Cirrus Guide II in situ database, is created from airborne in situ measurements during 150 flights in 24 campaigns. The archive contains meteorological parameters, ice water content (IWC), ice crystal number concentration (Nice), ice crystal mean mass radius (Rice), relative humidity with respect to ice (RHice), and water vapor mixing ratio (H2O) for each of the flights. Depending on the parameter, the database has been extended by about a factor of 5–10 compared to earlier studies. As one result of our investigation, we show that the medians of Nice, Rice, and RHice have distinct patterns in the IWC–T parameter space. Lookup tables of these variables as functions of IWC and T can be used to improve global model cirrus representation and remote sensing retrieval methods. Another outcome of our investigation is that across all latitudes, the thicker liquid-origin cirrus predominate at lower altitudes, while at higher altitudes the thinner in situ-origin cirrus prevail. Further, examination of the radiative characteristics of in situ-origin and liquid-origin cirrus shows that the in situ-origin cirrus only slightly warm the atmosphere, while liquid-origin cirrus have a strong cooling effect. An important step in completing the Cirrus Guide II is the provision of the global cirrus Nice climatology, derived by means of the retrieval algorithm DARDAR-Nice from 10 years of cirrus remote sensing observations from satellite. The in situ measurement database has been used to evaluate and improve the satellite observations. We found that the global median Nice from satellite observations is almost 2 times higher than the in situ median and increases slightly with decreasing temperature. Nice medians of the most frequently occurring cirrus sorted by geographical regions are highest in the tropics, followed by austral and boreal midlatitudes, Antarctica, and the Arctic. Since the satellite climatologies enclose the entire spatial and temporal Nice occurrence, we could deduce that half of the cirrus are located in the lowest, warmest (224–242 K) cirrus layer and contain a significant amount of liquid-origin cirrus. A specific highlight of the study is the in situ observations of cirrus and humidity in the Asian monsoon anticyclone and the comparison to the surrounding tropics. In the convectively very active Asian monsoon, peak values of Nice and IWC of 30 cm−3 and 1000 ppmv are detected around the cold point tropopause (CPT). Above the CPT, ice particles that are convectively injected can locally add a significant amount of water available for exchange with the stratosphere. We found IWCs of up to 8 ppmv in the Asian monsoon in comparison to only 2 ppmv in the surrounding tropics. Also, the highest RHice values (120 %–150 %) inside of clouds and in clear sky are observed around and above the CPT. We attribute this to the high H2O mixing ratios (typically 3–5 ppmv) observed in the Asian monsoon compared to 1.5 to 3 ppmv found in the tropics. Above the CPT, supersaturations of 10 %–20 % are observed in regions of weak convective activity and up to about 50 % in the Asian monsoon. This implies that the water available for transport into the stratosphere might be higher than the expected saturation value.

Albenis Pérez-Alarcón, Marta Vázquez, Alexandre M. Ramos et al.

Compound drought and heat wave events (CDHWs) are weather and climate hazards whose frequency is increasing in many regions across the globe. Here, we applied a novel Lagrangian atmospheric moisture and heat tracking framework to the outputs of the Lagrangian FLEXPART model driven by the ERA5 reanalysis to quantify the moisture and sensible heat flux anomalies for CDHWs occurred in the Iberian Peninsula in the extended summer (May–October) from 1991 to 2022. CDHWs are identified based on the 95th percentile of daily maximum and minimum temperatures and the self-calibrating Effective Drought Index. The Lagrangian framework is then applied to the top 20 CDHWs affecting more than 50% of continental Iberian Peninsula. Our analysis reveals that these events endure on average 10.35 days, with 2022 achieving the highest number of days (46 days) under dry and hot conditions. CDHW events are generally associated with blocking situations and high-pressure systems, whose effects can be amplified by the local land-atmosphere feedback. The results indicate that the Iberian Peninsula itself is the principal moisture source for the low summertime precipitation, followed by the North Atlantic Ocean corridor and the western Mediterranean Sea, but their total moisture contribution decreases by about 56% during the CDHWs. Moreover, the sensible heat sources pattern exhibits a local-to-regional origin, with ∼35% above the climatological value during the dry and hot events. Overall, this study provides new insight into the underlying mechanisms of CDHWs, which could be useful for helping in understanding these events in the context of global warming.

Deep S Banerjee, Jozef Skakala, David Ford

We demonstrate that combining machine learning with data assimilation leads to a major improvement in phytoplankton short-range (1-5 day) forecasts for the North-West European Shelf (NWES) seas. We show that excess nitrate concentrations are a major reason behind known biases in phytoplankton forecasts during late Spring and Summer, which can grow fast with lead time. Assimilating observations of nitrate would potentially help address this, but NWES nitrate data are typically not available in sufficient abundance to be effectively assimilated. We have therefore used a recently developed and validated neural network (NN) model predicting surface nitrate concentrations from a range of observable variables and implemented its assimilation within a research and development version of the Met Office's NWES operational forecasting system. As a result of nitrate assimilation the phytoplankton forecast skill improves by up to 30%. We show that although much of this improvement can be achieved by using a weekly nitrate climatology predicted by the NN model, there is a clear advantage in using flow-dependent nitrate data. We discuss the impacts of this improvement on a range of additional eutrophication indicators, such as dissolved inorganic phosphorus and sea bottom oxygen. We argue that it should be feasible to implement this hybrid machine learning - data assimilation approach within the near-real time NWES operational forecasting system.