Localized Weather Data for Accurate Precipitation Measurement

Local Weather Data x Critical Risk Management We talk a lot about environmental impacts on high-risk activities—like wind speed & direction impacting crane lifts, work at height, and heavy equipment operations—but how representative is the weather data we rely on? Most of the time, we use forecasted conditions from national meteorological services which are great for general awareness but often don’t reflect site-specific conditions. A forecast from a weather station 30km away doesn’t capture sudden wind gusts at a crane lift zone, temperature variations on-site, or microclimates created by terrain. Having local, real-time weather data at the actual worksite enables better risk management decisions. Instead of relying on broad forecasts, organisations can monitor live conditions at the precise location where critical work is happening. PLUS you get your own comprehensive data set for analytics... In the photos I'm holding a Davis EnviroMonitor Gateway LTE & Vantage Pro2 GroWeather Sensor Suite which is an example of a local weather monitoring system. This system provides real-time, hyper-local weather data directly from the worksite, enabling data-driven risk management decisions. It delivers real-time updates every 2.5 seconds; has wind speed, temperature, humidity, and rainfall monitoring plus solar radiation and evapotranspiration data which is also valuable for heat stress risk. This model has LTE connectivity (basically you can stick a SIM card in it) for remote monitoring and integration with cloud platforms. These systems aren't that expensive and offer new insights for local risk management that I've found can make a pretty big difference to your risk control strategy. Is anyone else implementing local weather systems for crane ops or other critical risk management? #safetytech #safetyinnovation #IoT

🌍 Exploring Daily Precipitation Data Using Google Earth Engine 🌧️ In a recent analysis, I worked on calculating daily precipitation data over the Northeast region of Brazilusing Google Earth Engine. By leveraging satellite data from the TRMM (Tropical Rainfall Measuring Mission), I created a time series for 2019, allowing us to visualize and analyze rainfall patterns over time. This script automates the process of filtering precipitation data, calculating daily sums, and creating a clear visual time series chart. With the increasing need for detailed environmental monitoring, tools like Google Earth Engine make it easier to access, process, and analyze large datasets for environmental research and conservation efforts. 💻 Key steps in the code: Define the area of interest. Filter precipitation data from the TRMM dataset. Calculate daily precipitation totals. Visualize the data in a time series chart. 🌟 Why is this important? Understanding precipitation patterns is critical for monitoring climate change, managing water resources, and protecting ecosystems. This script can be adapted for any region, helping researchers and conservationists gain insights into local and global rainfall trends. 🔗 Explore how to integrate remote sensing into your workflows and make data-driven decisions for a more sustainable future! #GeospatialAnalysis #GoogleEarthEngine #RemoteSensing #EnvironmentalMonitoring #ClimateChange #Conservation #PrecipitationAnalysis #SpatialData #DataScience #GIS #EarthObservation #Sustainability #ResearchAndDevelopment #TRMM #RainfallPatterns See the code bellow: // Define the area of interest (AOI) var roi = ee.FeatureCollection("place your roi here"); Map.centerObject(roi); // Draw the outline of the AOI var empty = ee.Image().byte(); var outline = empty.paint({  featureCollection: roi,  color: 'red',  width: 2 }); Map.addLayer(outline, {palette: ['red']}, 'Roi area'); // Define the start and end dates var startDate = ee.Date('2019-01-01'); var endDate = ee.Date('2022-01-01'); // Define the ImageCollection (TRMM precipitation data) var precipitation = ee.ImageCollection("TRMM/3B42")            .select('precipitation') // Select the precipitation band            .filterDate(startDate, endDate); //**** Daily Data Calculation // Calculate the number of days between the start and end dates var nDays = ee.Number(endDate.difference(startDate, 'day')); print('Number of days:', nDays); // Create a list of sequential days var daysList = ee.List.sequence(0, nDays.subtract(1)); // List of days between start and end dates // Map the list of days to calculate daily precipitation sums var dailyPrecipitationList = daysList.map(function(dayN) {  // Define the start time (t1) for each day  var t1 = startDate.advance(ee.Number(dayN), 'day');    // Define the end time (t2) for each day (one day later)  var t2 = t1.advance(1, 'day');    other parts of the code, down in the comments

🌧️ Across Africa, many engineers face a recurring challenge — limited or unreliable rainfall data from local meteorological stations. Yet, accurate rainfall information remains the cornerstone of stormwater design and urban flood management. 💡 1. Is the Use of Satellite Rainfall Estimates Encouraged? Absolutely — with caution and calibration. Satellite-based rainfall estimates (such as CHIRPS, GPM-IMERG, or TRMM) have revolutionized how we model catchment response in data-scarce regions. They provide spatially continuous, near real-time rainfall data, ideal for modelling where ground gauges are few or non-existent. For instance, in Chipinge and Muzarabani, local gauges often fail during storms, but satellite data provides continuous records that can be cross-checked against manual readings. When calibrated with a few ground observations, these datasets yield rainfall inputs accurate enough for HEC-HMS, SWMM, or EPA SWAT simulations — enabling realistic runoff predictions and flood mapping. ✅ Key takeaway: Use satellite rainfall data as a base dataset, but always apply bias correction or statistical validation against local records before integrating into hydrologic models. 📈 2. How to Develop an Intensity–Duration–Frequency (IDF) Curve Here’s a concise workflow: 1. Collect Rainfall Data: -Obtain daily/hourly rainfall data (from gauges or corrected satellite datasets). 2. Extract Annual Maxima: -Identify the highest rainfall event for each year for various durations (5 min, 15 min, 1 hr, etc.). 3. Perform Frequency Analysis: -Fit statistical distributions (e.g., Gumbel, Log-Pearson Type III) to estimate rainfall depths for different return periods (2, 5, 10, 25, 50 years). 4. Develop the Equation: ( i = a / (t + b)^c ) where i = intensity, t = duration, and a, b, c are coefficients derived via regression. 5. Plot and Validate: -Create the IDF curve, compare with regional design charts (e.g., ZINWA or WMO guidelines), and calibrate if discrepancies arise. 💧 Example: In the Mutare urban stormwater upgrade, an IDF curve derived from 15 years of CHIRPS data (bias-corrected using EMA gauge data) produced rainfall intensities closely matching historical floods — helping engineers redesign culverts that previously failed under underestimated flows. 🌍 Final Thought As we move toward climate-resilient infrastructure, engineers must blend traditional hydrology with modern data technologies. Satellite rainfall and data-driven IDF curves are not replacements but powerful complements to conventional methods — especially in regions where every millimeter of rainfall counts. 🔹 Civil Legacy Consultancy continues to integrate innovative hydrologic modelling and GIS-based analytics to enhance water and stormwater system designs across Zimbabwe and beyond. #CivilLegacyConsultancy #Hydrology #StormwaterDesign #DataDrivenEngineering #HECHMS #SWMM #ClimateResilience #CivilEngineering #WaterInfrastructure

📘 Downscaling CHIRPS Precipitation Data to 100m Resolution Using Sentinel-2 in Google Earth Engine Source Code = https://lnkd.in/dNc7NbjE 1. Introduction: Rainfall data at high spatial resolution is critical for precise hydrological analysis, drought monitoring, and agriculture planning. However, most global precipitation datasets, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), are available at coarser resolutions (~5 km). This project addresses this limitation by downscaling CHIRPS daily precipitation data to 100-meter spatial resolution using bilinear interpolation and Sentinel-2 as a high-resolution spatial reference. 2. Objective: To extract and sum CHIRPS precipitation data over a selected AOI (WMH District) for a specific 3-month period (October 2023 – January 2024). To downscale the CHIRPS raster data to a finer 100-meter resolution using Sentinel-2 spatial referencing. To visualize and compare the original and downscaled precipitation maps. To prepare refined precipitation layers for potential integration with NDVI, crop condition analysis, or drought indices. 3. Importance of the Study: Higher spatial resolution enables more localized analysis of rainfall, especially in heterogeneous landscapes. Improved input for climate models and agro-hydrological studies. Better decision-making for irrigation scheduling, water resource management, and drought preparedness. Supports integration with high-resolution datasets such as NDVI, land use, or soil moisture for multi-parameter environmental studies. 4. Benefits: Enhanced accuracy in rainfall data analysis at local and district levels. Scalable method applicable to any region globally. Supports policymakers and researchers with higher-resolution inputs for climate resilience, agricultural planning, and hydrological monitoring. Efficient use of cloud computing via Google Earth Engine for handling large spatiotemporal datasets. 5. Output: Original CHIRPS Precipitation Map (Oct 2023 – Jan 2024) clipped to WMH District. Downscaled Precipitation Map at 100m resolution, reprojected using Sentinel-2 reference. Color-coded visualization using a 5-class blue gradient, where: Light blue = Low precipitation Dark blue = High precipitation Ready-to-export raster layer of downscaled precipitation (if export added). Output maps can be further used for vegetation correlation (e.g., NDVI vs. rainfall) and SPI generation. #GEE #GoogleEarthEngine #BuildupAreaExpansion #GeospatialAnalytics #RemoteSensing #UrbanExpansion #Geospatial #GoogleEarthEngine #GIS #SustainableDevelopment #Sentinel2 #GeospatialTech #PhD #Agriculture #ClimateSmart #GIS #DeepLearning #ClimateSmartAgriculture #CropHealthMonitoring #DroughtMonitoring #SustainableFarming #Sentinel2 #GoogleEarthEngine #NDVI #LandsatData #GISMapping #GeospatialAnalysis #AIinAgriculture #EarthObservation #AgricultureMapping #RemoteSensin #SatelliteImagery

Did you know the Brazilian National Institute for Space Research (INPE) offers daily precipitation rasters that are calibrated for South America? As we approach the impending El Nino phenomenon in 2023, the reliance on accurate climatological data is more critical than ever. INPE's product, named MERGE, is built upon the IMERGE/GPM model. However, it distinguishes itself by utilizing thousands of in-situ rain gauges to ensure unbiased results. I'm thrilled to share my latest article on #towardsdatascience publication, introducing the merge-downloader package, called Harnessing Precipitation and Climatological Raster Data in South America (https://lnkd.in/dSRJ3jE5) This tool has been specifically designed to simplify the downloading and extraction of time series from this invaluable data. This can be particularly useful for a number of applications such as watershed and reservoir management, critical events monitoring and precision agriculture. Take advantage of this package and enhance your understanding of our changing climate. #datascience #geospatialintelligence #weather #elnino