gspatialhistoricaldata

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Optimized Bulk Historical Weather Data To Design Climate Solutions

Hyper-local historical weather data for every point on the globe, collected, validated, and processed to get insights that will transform the way you make climate decisions.

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Trustworthy, accurate, granular, and hyperlocal historical air quality data, collected from multiple satellites and validated to solve air pollution and minimize climate risks.

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gspatial.ai is a cognitive mapping platform that combines artificial intelligence (AI) technology and human expertise to make information accessible and usable.

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The historical weather data prediction can be relied upon when we need to know what the weather will be like in the coming weeks. Standard weather predictions are based on computer models that predict how the weather will change in the next few weeks. Storm Impact Analytics makes forecasts based on your specific utility. This aim necessitates having information on your assets, infrastructure age, and vegetation management. Learn more about historical weather data at gspatial.ai.

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How Historical Weather Data Can Reduce Losses in Agriculture

In agriculture, weather forecasting is a deciding factor, supporting farmers harvesting healthy and abundant produce. The important meteorological parameters for agriculture include estimated precipitation and temperatures and historical weather data to organize field operations from planting to harvesting, including fertilizer or herbicide applications. Some chemicals must be applied on a dry day, while others require moisture to work. Furthermore, each crop necessitates a distinct soil temperature for sowing. Unexpected rains during harvest season wreak havoc on crops. Farmers will not be able to get the greatest results unless they have access to a dependable agriculture weather forecast that saves money and reduces risk.

Why Doesn't Public Weather Sufficiently Meet Agricultural Needs?

The significance of weather forecasting for agriculture explains why farmers are so eager to obtain the most up-to-date and precise information possible. With so many open-access public sources, this could appear that they can receive agricultural, historical weather data from any of them. Experienced farmland owners, on the other hand, choose to trust dependable providers for a variety of reasons, the most important of which is the data precision, which allows them to increase profit while lowering costs.

In general, agricultural weather is gathered from meteorological stations that cover a specific area surrounding them, and the greater the distance, the less precise the forecast. Forecasting methods commonly used enable prognosis based on factors and their combinations. On the other hand, such recommendations are far from exact and cannot always help make the best judgments and mitigate weather risks for agriculture.

That's why public information is insufficient for agricultural purposes. For example, suppose crops on a specific field don't get enough moisture even though rain is forecast in the area.

Weather analytics in agriculture is provided by EOS Crop Monitoring, which collaborates with Where to provide high-precision forecasts. Sensed data and stations are distributed globally, and a smaller, more concentrated operation distance, local topography analysis, and a complex algorithm for historical weather data processing are employed to achieve precision.

The disparities between information from public sources and information from specific sources can be rather impressive. For example, in terms of the maximum temperature monitoring over North America & Sub-Saharan Africa, a check of where numbers vs. NASA Power Grid indicates the following. Because of its 9 km resolution, higher altitude model, and curvilinear temperature and humidity interpolation provide more accurate meteorological monitoring findings in both scenarios.

Weather Data Accuracy and 'Hyper-Locality'

In agriculture, super local information is vital since it provides the highest level of accuracy. Crop Monitoring's weather monitoring in agriculture is based on several where agriculturally relevant sources:

In-field stations worldwide; Doppler radar; open-access & proprietary satellite historical weather data enable high-resolution agricultural weather forecasts.

A farmer may monitor the present environmental parameters of each field separately using Crop Tracking software, including temperature or cloud cover. Humidity, wind speed, and precipitation also are available as relevant indications. The software allows you to choose between metric and imperial measurements for the convenience of usage.

The benefit of the agriculture weather reports accessible in Crop Monitoring is that they will be based on multiple sources rather than just one closest to the questioned field. The analytics are based on complex algorithms that select the most relevant data from various sources such as radar, sensors, satellites, and weather stations. In addition, the algorithm considers local topography differences and applies adiabatic corrections to elevation discrepancies between stations and the area of focus. The procedures are used to provide the maximum level of data precision. In collaboration with Where, Crop Monitoring reports accurate temperatures of +/-0.58°C for the US Corn Belt & +/-0.68°C for the rest of the world.

Accurate Predictions

Weather patterns are changing these days, and one of the key factors in evolving agricultural methods is global climate change. That's why historical weather data analytics are so critical in agriculture. Crop output increases are ensured through accurate and dependable reports. They let farmers immediately respond to changing conditions, manage hazards, and organize field events in the most effective way possible. Crop Monitoring not only displays current meteorological conditions as "Weather Today," but also farm weather forecasts for up to fourteen days.

Weather in the Past

Aside from current field conditions and reliable projections for the next two weeks, Crop Monitoring also provides farmers with historical weather data on temperature and rainfall (daily and cumulative).

From 2008 until the present, the software creates historical weather data charts & allows weather tracking within agriculture every five years. Farmers can track certain climatic patterns in the chosen region this way. Known historical tendencies & established patterns enable agriculture to make better predictions and decisions on the best types of crops.

Soil fertility, weather conditions, and high-quality planting material are important elements in farming success. While choosing vibrant plants and boosting soil fertility are relatively simple tasks, managing meteorological threats proves to be the most difficult. Extreme weather events such as hail, floods, extreme cold, or heat can wipe out crops in a single day, as well as a reliable forecast encourages a timely reaction. Farmers can be alerted to the hourly weather of frost or droughts with Crop Monitoring.

Another thing to consider in agriculture would be that temperature differences of one degree from the prediction within a day have little impact on vegetation. On the other hand, Plants are severely harmed by cumulative occurrences such as unfavorable soil temperature, constant waterlogging, and protracted heat or cold stress. As a result, agribusiness stakeholders must consider daily meteorological circumstances and accumulated values over a week or two.

Crop Monitoring provides all the necessary data types for accurate weather analysis in agriculture, allowing farmers to succeed. Begin tracking the fields with us right away.

Accurate Weather Data At Your Fingertips

Ambee provides accurate weather data and information and includes various parameters such as temperature, pressure, humidity, wind speed and direction, cloud coverage, visibility, and dew point. Combining this information with proprietary artificial intelligence and machine learning technologies, intelligence can be generated with high accuracy and efficiency. With the help of Ambee’s weather API, monitoring the weather, soil, and scheduling the most appropriate time for any agricultural activity becomes prompt and easy. Ambee also provides historical bulk weather data through their product Gpastial.ai. 

To learn more, visit www.getambee.com.

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Can We Predict Extreme Weather Events with Historical Weather Data?

How to forecast the weather on any day of the year using historical weather data?

The historical weather data prediction can be relied upon when we need to know what the weather will be like in the coming weeks. Standard weather predictions are based on computer models that predict how the weather will change in the next few weeks. What happens, however, if you aren't interested in the coming weeks? Perhaps you're organizing a wedding, a vacation, or an outdoor event and need to know what the historical weather data will be like at a specific area and date in the future. In that instance, we utilize historical weather measurements gathered over a long period to assist us in predicting the weather we would likely encounter.

What kind of statistical weather data are we looking for?

We need to discover the typical weather patterns for that location while developing statistical weather data-based forecasts. For example, we'd want to say the average high and low temperatures and the likelihood of rain. This, however, does not provide us with the complete picture. If I'm arranging a vacation, I'll need to understand the average weather or the worst and best scenarios. It's fascinating to learn about previous weather 'normals,' but we also need to understand how likely the weather will be much hotter or colder than average. We need to see the big picture - what is regular weather, is this the worst weather that might happen, or what is the best weather? And what are the chances of more extreme weather occurring?

Obtaining weather data from the past

The NWS provides historical weather data in various forms for others to acquire and use. Some of these are the National Climatic Data Center (NCDC), NOAA Environmental Modeling System, and the Cooperative Observer Program (COOP). These groups are regarded as some of the most historically important historical weather data sources. The National Center for Environmental Information, for example, is a federal agency that collects and protects environmental data using observational records from throughout the world. They also study various atmospheric phenomena, such as climate change and variability. The Environmental Modeling System of the National Oceanic and Atmospheric Administration (NOAA) is another source (EMC). To forecast the weather, EMC uses a variety of models. A mathematical analysis that uses historical data is one type; other forms include numerical forecasting models that can be used for short-term projections and the Cooperative Observer Program (COOP), which has over 12,000 reporting sites in North America and Hawaii alone, is another. Raw data, on the other hand, is insufficient. To make forecasts, the data must be evaluated and interpreted. Utilities must give statistics meaning and context before applying them to their specific infrastructure area. Creating predictive models

Predictive models are constructed by "teaching" the model what has occurred in a certain location using available data. This historical weather data information is extrapolated to create models for predicting future situations. Models for a variety of scenarios will be constructed using machine learning.

When faced with a severe weather disaster, these situations will help you make judgments. It will influence the type of mutual aid you implement, the locations where workers are deployed, and how you distribute your resources.

This is where meteorologists use historical weather data from previous weather events to forecast future weather patterns. Because the data set is tiny, the model will learn from only a few real-life instances. On the other hand, what if that year contained characteristics that were out of the ordinary for your area?

What if, for example, that year had a lot of rain, and you're predicting the weather for next month? If the model hasn't learned that those circumstances were out of the ordinary, it will make assumptions based on what was previously out of the ordinary in your location.

This implies you can't predict how much rain would fall during specific months or what is considered "normal" for your area. You'd then make judgments based on a faulty data set, perhaps jeopardizing people’s lives.

As a result, the more information you have, the more precise your forecasts will be. Ongoing refinements The work of predicting the weather or developing these models cannot be completed in a single sitting. Rather, it is a continuous process that necessitates regular refinement. The models can learn and be more accurate over time as you continue to add fresh data. Organizations must compare this new data to historical weather data to establish what is normal, atypical, or even the beginning of a new trend. As a result, you'll want to make sure your weather forecast model continues to add data as it learns and improves. Using historical data

It is becoming increasingly vital to develop more efficient means of dealing with power disruptions. Power outages due to weather-related occurrences have grown by 67 percent in the United States since 2020.

Many utilities acknowledge the importance of advanced weather analytics, but many are hesitant to invest in the technology.

That hesitancy and reluctance are frequently due to three factors:

•         Prior experience in poor data collecting.

•         A lack of seamlessly with existing systems; and

•         A lack of skill set required to interpret raw data.

Storm Impact Analytics offers unrivaled customer service and historical weather data. Furthermore, our customers have faith in our forecasts and the assistance they get from your team of expert meteorologists.

In addition, Storm Impact Analytics makes forecasts based on your specific utility. This aim necessitates having information on your assets, infrastructure age, and vegetation management. 

Learn more about historical weather data at gspatial.ai.  

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Gspatial.ai delivers information on climate change risks, all the way from space, in a frictionless environment. A team of engineers, scientists, designers, and industry experts have spent almost every year working on this product that helps humankind prepare for climate change effects, shields businesses from climate risks, and allows everyone to make informed decisions

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Gspatial.ai provides the most extensive historical pollen data for more than 150 countries.Get instant access to historical pollen count data by zip code. Download now

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Optimized #HistoricalWeatherData by date for any zip code or city. Our Gspatial’s weather historical intelligence offers Accurate, Accessible, Insightful data all the way from 1991. Hyper-local historical weather data for every point on the globe, collected, validated, and processed to get insights that will transform the way you assess climate risks.

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Gspatial.ai offers bulk historical air quality data, globally for the past 20+ years. Download historical AQI data by city in just a few clicks.

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Optimized Historical Weather Data by date for any zip code or city. Our Gspatial’s weather historical intelligence offers Accurate, Accessible, Insightful data all the way from 1991.