Real-world Meteorology Research Methods

Four complementary approaches could collectively predict the "unprecedented" in weather, informing disaster preparation. Climate change is increasing the frequency and intensity of record-breaking weather events worldwide, from heat domes to unseasonal floods. These events often catch communities unprepared because they exist beyond our lived experience and historical records. A new perspective provides an overview of scientific approaches to identify unprecedented weather before it occurs, informing emergency management. The research team identified four complementary lines of evidence that together provide a robust framework: conventional statistical methods using observations, analysis of past events from historical records and proxies, event-based storylines, and weather/climate model explorations. When applied together — as demonstrated in their case study of extreme heat in the Netherlands — these approaches revealed that temperatures of up to 48°C are physically possible in regions previously thought to have maximums below 40°C. This work has significant implications for building climate resilience, which the authors conceptualize as a pyramid with transformative adaptation as the foundation, supported by incremental infrastructure improvements and reactive early warning systems. By Timo Kelder, Dorothy Heinrich, Lisette Klok, Vikki Thompson, Henrique Goulart, Ed Hawkins, Louise Slater and al.

Global #precipitation products leave gaps and biases, especially over mountains and at high rain rates. Check this new paper in Communications Earth & Environment where we introduce an open-access framework that merges multi-source observations using regional-scale intelligent optimization and explicit #topographic factors within an end-to-end neural pipeline. The approach reconstructs missing values and corrects biases in global, time-varying precipitation fields, yielding stronger correlations and reduced errors versus standard satellite estimates. Results suggest immediate value for #storm monitoring and #flood forecasting in data-sparse, complex terrain. Paper: Communications Earth & Environment (2025), doi:10.1038/s43247-025-02624-3. https://lnkd.in/dKp6NsyY Hohai University Consiglio Nazionale delle Ricerche

In 2022, I was part of a research team that took on a critical challenge: 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐬𝐬𝐞𝐬𝐬 𝐫𝐚𝐢𝐧𝐟𝐚𝐥𝐥 𝐢𝐧 𝐫𝐞𝐠𝐢𝐨𝐧𝐬 𝐰𝐡𝐞𝐫𝐞 𝐫𝐚𝐢𝐧 𝐠𝐚𝐮𝐠𝐞𝐬 𝐚𝐫𝐞 𝐬𝐜𝐚𝐫𝐜𝐞? Our focus was Sudan, a country where climate-driven decisions are crucial, but data gaps make it difficult to plan and prepare. We turned to satellite and gridded rainfall datasets, tools that have become essential in modern hydroclimatic research. What we found was both promising and eye-opening. 📍 While most rainfall products showed a tendency to underestimate rainfall, especially on annual and monthly scales, two stood out: 𝐂𝐇𝐈𝐑𝐏𝐒 𝐚𝐧𝐝 𝐂𝐑𝐔 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬 consistently performed best, especially in the western and southern regions of Sudan. 🌧️ We discovered that summer rainfall (the main rainy season) is captured more accurately than annual totals, especially in mountainous areas. And when we explored deeper, we noticed a significant link between rainfall trends and the Atlantic Multidecadal Oscillation (AMO), with some regions showing correlations as high as 90%. This was more than just data and numbers; it was a reflection of how remote sensing, when used wisely, can support 𝐜𝐥𝐢𝐦𝐚𝐭𝐞 𝐫𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞, 𝐟𝐨𝐨𝐝 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐫𝐢𝐬𝐤 𝐩𝐫𝐞𝐩𝐚𝐫𝐞𝐝𝐧𝐞𝐬𝐬 in the very places that need it most. 🔍 The study underscored the value of CHIRPS data for monitoring rainfall variability and extreme events, and why it should be a go-to resource for decision-makers in data-scarce environments. Being part of this work reminded me of the power of science and collaboration in driving evidence-based action. It’s research like this that fuels real-world solutions, and I’m proud to have contributed to it.  Richard Anyah Mohamed Abdallah Ahmed Alriah, PhD Aslak Grinsted Hans Hersbach Lorenz Ewers Victor Ongoma Nixon Mutai #ClimateResearch #RainfallData #RemoteSensing #Sudan #ClimateResilience #CHIRPS #Hydroclimatology #SatelliteData #ClimateScience #DataForDevelopment

Good morning, LinkedIn Community! Today marks Day 2 of our series showcasing the capabilities of the Weather Research and Forecasting model (#WRF), developed by NSF NCAR - The National Center for Atmospheric Research, and its impact on countries worldwide. At the request of Dame Gueye, I've conducted a 72-hour forecast model over #Senegal, a country distinguished by its unique climate with two distinct seasons: a dry season from October to May and a rainy season from June to September. Senegal's climate is significantly influenced by its tropical latitude and the seasonal shifts of the intertropical convergence zone (ITCZ). This low-pressure front where hot, dry continental air meets moist ocean air is pivotal in causing the region’s heavy rainfall. A challenge in global meteorology is the sparsity of reporting stations, particularly in regions like Senegal. Here, the WRF model excels. Despite the global practice of launching radiosondes at 00Z and 12Z to capture atmospheric dynamics, the lack of upper-air stations in many areas leaves a gap filled by numerical weather models. These models, including WRF, generate "model soundings" that simulate data as if weather balloons were launched locally. For this demonstration, I've run simulated upper-air soundings for the five most populous zones in Senegal using the WRF model.  Dakar Capital and largest city  Touba Second largest city Thiès Third largest city   Kaolack  Fourth largest city   Mbour  Fifth largest city This ability to create detailed atmospheric profiles enhances local forecasters' capabilities by providing a dynamic snapshot of the atmosphere according to the deterministic outputs of the model. Examining the atmospheric maps, we observe how the #ITCZ is currently triggering widespread showers and storms across the southern part of Senegal. It's crucial to note that WRF is a deterministic model which relies on a set of physics and dynamic schemes for a "best guess" atmospheric representation over its domains. In this specific model run, adhering closely to NCAR’s 'Tropical' physics suite, I only deviated by disabling the cumulus schemes, which are approximations for non-convective resolving grid spaces, for grid resolutions smaller than 5x5km to allow the model to explicitly resolve convection. The implementation of regional weather models could be highly beneficial for island nations and countries which do not have high performance computers or clusters, enhancing the forecasting capabilities and resources for the member states of #WMO. If you believe in the potential of regional modeling as I do, please reach out to your permanent representative (https://lnkd.in/gHJvHUwh). Thank you for joining me in exploring the WRF model’s potential. If you have any questions or wish to see your country featured in upcoming posts, please leave a comment below! #Meteorology #Weather #Forecasting #Atmospheric #Science #WMO #NCAR #UCAR #NWS #NWP #Monsoon #Rain #EarlyWarningsForAll

Surface tower-based measurements are often used as a reference observation for satellite data calibration- validation studies, for developing as well as evaluating model (simulation) results, and for advancing understanding of the earth system processes. An important consideration at the heart of such measurements is understanding what is the surface the results are representative of. For this, there is a body of models that are integrated to understand the so-called source area and footprint estimates. Assumption about a homogeneous surface is inherent in these models - something which is hard to satisfy especially in places close to cities, and in regions of Indian, and African subcontinent. To address this question, we conducted field measurements and tested different models, the results from which have been summarized in a recent a paper Shweta Kumari, B. V. N. P. Kambhammettu, Mark A. Adams, and Dev Niyogi. "Analysis of flux footprints in fragmented, heterogeneous croplands." Meteorology and Atmospheric Physics 136, no. 2 (2024): 9. available at https://lnkd.in/gnSdNERK