1) Data Introduction • The Weather Dataset contains daily weather observations collected from various cities around the world. It includes key meteorological indicators such as temperature, humidity, wind speed, pressure, and weather conditions.

2) Data Utilization (1) Characteristics of the Weather Dataset: • The dataset consists of multivariate weather data organized by city and date, making it suitable for time-series analysis and climate-related prediction model development.

(2) Applications of the Weather Dataset: • Development of time-series weather prediction models: The dataset can be used to train models such as LSTM or ARIMA based on features like temperature, humidity, and precipitation.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

1) Data Introduction • The Multi-class Weather Dataset is a computer vision dataset for multi-class image classification, designed to classify various weather conditions based on static images. It consists of four distinct classes (Cloudy, Rain, Shine, Sunrise), each representing visually distinguishable weather conditions suitable for classification model training.

2) Data Utilization (1) Characteristics of the Multi-class Weather Dataset: • The dataset consists of images captured in real outdoor environments, making it advantageous for developing practical models that reflect various weather conditions.

(2) Applications of the Multi-class Weather Dataset: • Weather Image Classification Model Development: This dataset can be used to train deep learning models that automatically recognize various weather conditions from images. • Smart Surveillance / Autonomous Driving Perception Research: It can also be applied to the development of real-time monitoring systems or perception systems in autonomous vehicles, particularly to evaluate recognition performance under varying weather conditions.

DPIRD's network of automatic weather stations throughout the state provide timely, relevant and local weather data to assist growers and regional communities make more informed decisions. This data includes air temperature, humidity, rainfall, wind speed and direction, with most stations also measuring incoming solar radiation to calculate evaporation. The weather stations report to DPIRD’s website every 10 minutes and provide near real time data. Local weather, such as rainfall or frost, can vary widely over short distances. The weather stations are sited to provide good geographical coverage. Their data is sent to the Bureau of Meteorology for use in weather forecasting and climate studies. Industry uses this data for time critical agribusiness decision-making. These can range from scheduling irrigation of vegetable crops, through to choosing the right conditions for spraying operations in broad scale crops. DPIRD weather data is available to developers of decision support tools or weather applications via our Application Programming Interfaces (APIs).

Here are a few use cases for this project:

1. "Meteorological Research": Researchers or meteorologists could use "cloud" to analyze weather patterns by identifying and categorizing different types of cloud formations based on their visual structures. This could develop a better understanding of weather patterns, climate changes, and predict weather phenomena more accurately.

2. "Weather Forecasting Applications": The model could be integrated into weather forecasting applications to provide real-time updates about potential weather condition shifts depending upon the pattern of clouds observed.

3. "Climate Education": Education tech applications could use this model to help students learn about different categories of clouds in an interactive way, aiding their understanding of weather systems and climate science.

4. "Aerial and Satellite Imagery Analysis": This model could be used to analyze satellite or drone images to provide insights into geographical and climate phenomena, helping in forest management, disaster management, agriculture sector, etc.

5. "Aviation Safety": The aviation industry could use the "cloud" model to analyze cloud formations and anticipate weather conditions to ensure the safety and efficiency of flight routes.

This dataset contains 4 Essential Climate Variables (ECV) for the 18 bias adjusted Global Climate Models (GCM) from CMIP5: daily precipitation rate, and daily mean, maximum and minimum temperatures. The data are bias adjusted using the Distribution Based Scaling (DBS) method versus the global reference dataset HydroGFD2.0, both bias adjustment method and global reference dataset developed by the Swedish Meteorological and Hydrological Institute (SMHI). The DBS method is a parametric quantile-mapping variant. This type of methods fit a statistical distribution to the cumulative distribution function and use those fitted distributions to conduct the quantile-mapping. Here, we used a double-gamma distribution (i.e. separate gamma distributions for the bulk and the high tail) for precipitation and the normal distribution for all temperature variables. Temperature corrections were done conditional on the wet/dry state of the corresponding precipitation time series. The seasonal variations in the biases were represented by monthly parameter windows for precipitation and a smoothed seasonal cycle for the temperature distribution parameters. The smoothing was done using twelve harmonic components. There is some post-processing in place for the data set to be suitable for hydrological impact modeling. Bias-adjustment of daily mean, maximum and minimum temperature using quantile mapping can in some cases lead to inconsistencies. For instance, maximum (minimum) temperature could be lower (higher) than mean temperature. If such inconsistencies occur, daily minimum and maximum temperatures are adjusted in such a way that the anomalies with respect to the daily mean temperature are in line with the climatological anomalies for the particular day in the seasonal cycle. This means, for example, that an inconsistency occurring on June 25 will be adjusted using the climatological anomalies for June 25, estimated by a moving window. Also, the adjustment is done conditional on the wet/dry state of the corresponding precipitation series. The climatology of the anomalies was derived from the HydroGFD2.0 dataset. In addition, DBS’s limitations lead to some data not being bias-adjusted or values beyond physically plausible ranges. In those cases, DBS gives missing values as output. Grid cells where the DBS method resulted in such missing values have been interpolated in time or space. If interpolation was not possible, full time series from nearest grid cell was copied to relating grid point.

The "Daily_Rainfall_data_from_India_Meteorological_Department_Agency_during_January_2024" dataset provides detailed information about daily rainfall measurements recorded by the India Meteorological Department (IMD) Agency throughout January 2024. The dataset likely includes data points such as date, location or station where rainfall was measured, rainfall amount in millimeters or centimeters, and possibly additional metadata such as weather conditions during the measurements.

This dataset can be valuable for various analyses and applications, including but not limited to:

1. Rainfall Patterns: Analyzing the daily rainfall patterns to understand trends, variability, and anomalies in precipitation during January 2024 across different regions in India.

2. Weather Forecasting: Utilizing historical rainfall data to improve short-term and long-term weather forecasting models, which can be crucial for agricultural planning, disaster preparedness, and infrastructure management.

3. Climate Studies: Incorporating the rainfall data into broader climate studies to assess the impact of climate change on precipitation patterns over time.

4. Water Resource Management: Assessing the impact of rainfall on water resources, including groundwater recharge, reservoir levels, and water availability for various sectors like agriculture, industry, and domestic use.

5. Risk Assessment: Using rainfall data to assess the risk of floods, landslides, and other natural disasters associated with heavy precipitation events.

Overall, this dataset serves as a valuable resource for researchers, meteorologists, policymakers, and various stakeholders interested in understanding and leveraging rainfall data for a wide range of applications and studies related to weather and climate in India.

Dataset Description

This dataset is designed to predict taxi trip fares based on various factors such as distance, time of day, traffic conditions, and more. It provides realistic synthetic data for regression tasks, offering a unique opportunity to explore pricing trends in the taxi industry.

Key Features - Distance (in kilometers): The length of the trip. - Pickup Time: The starting time of the trip. - Dropoff Time: The ending time of the trip. - Traffic Condition: Categorical indicator of traffic (light, medium, heavy). - Passenger Count: Number of passengers for the trip. - Weather Condition: Categorical data for weather (clear, rain, snow). - Trip Duration (in minutes): Total trip time. - Fare Amount (target): The cost of the trip (in USD).

Dataset Highlights - 1,000+ rows of data, ensuring a robust sample for model training. - Includes missing values and outliers for realistic preprocessing challenges. - Introduces correlations among some features to simulate real-world scenarios. - Suitable for regression analysis and feature engineering exercises.

Possible Applications - Predicting taxi fares based on distance, traffic, and weather. - Exploring the impact of time and conditions on pricing. - Building regression models and comparing their performance.

Get started by analyzing the data, engineering features, and building models to optimize taxi fare predictions. Good luck!

To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023. Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024). BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.

The Airborne Doppler Wind LiDAR market is experiencing robust growth, projected to reach $751 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 7.4% from 2025 to 2033. This expansion is driven by the increasing demand for accurate and reliable wind data across various sectors. The rising adoption of renewable energy sources, particularly wind power, necessitates precise wind profile measurements for efficient energy harnessing and turbine placement optimization. Furthermore, advancements in LiDAR technology, leading to more compact, efficient, and cost-effective systems, are fueling market growth. Meteorological applications, crucial for weather forecasting and atmospheric research, also contribute significantly to market demand. Competition is intense, with established players like Vaisala and emerging companies like Windar Photonics A/S and Quantum Systems vying for market share through technological innovation and strategic partnerships. The market's segmentation is likely driven by application (renewable energy, meteorology, aviation etc.), LiDAR type (e.g., scanning vs. non-scanning) and geographic regions. Growth is anticipated to be geographically diverse, with North America and Europe likely maintaining significant shares, while Asia-Pacific regions exhibit strong growth potential due to increasing investments in renewable energy infrastructure.
The market's restraints primarily involve high initial investment costs associated with LiDAR systems and the dependence on favorable weather conditions for optimal data acquisition. However, ongoing technological advancements, coupled with decreasing manufacturing costs, are mitigating these challenges. The forecast period (2025-2033) holds significant opportunities for market players focusing on developing enhanced data processing capabilities, improved system reliability, and integrating LiDAR data with other meteorological information sources. This integrated approach provides a more comprehensive and valuable dataset for various applications, reinforcing the long-term growth prospects of the Airborne Doppler Wind LiDAR market.

Here’s a detailed description for updating and improving your crop recommendation system based on soil data:

Description of a Crop Recommendation System with Soil Data

A crop recommendation system helps farmers select the best crops to grow based on the specific properties of their soil. This system uses soil characteristics and environmental factors to determine the crops that are most likely to thrive. Recommendations are provided to improve crop yield, optimize resource use, and ensure sustainable farming practices.

Core Components for Recommendations

The system should consider the following soil parameters and external factors to make accurate recommendations:

1. Soil Nutrients:

   • Nitrogen (N): Promotes leafy growth; ideal for crops like spinach, lettuce, and wheat.
   • Phosphorus (P): Essential for root development; crucial for legumes, peas, and root vegetables like carrots.
   • Potassium (K): Enhances disease resistance and fruit quality; important for fruiting plants like tomatoes, bananas, and potatoes.

2. Soil pH:

   • Indicates soil acidity or alkalinity.
   • Neutral pH (6.5-7.5) supports most crops like rice, wheat, and maize.
   • Acidic soil (<6.5) favors crops like tea and coffee.
   • Alkaline soil (>7.5) supports crops like barley and asparagus.

3. Organic Matter:

   • High organic content improves water retention and nutrient availability.
   • Crops like vegetables and fruits benefit from rich organic matter.

4. Moisture Level:

   • Determines irrigation needs and crop suitability.
   • High moisture crops: Paddy, sugarcane.
   • Low moisture crops: Millet, sunflower.

5. Temperature:

   • Warm crops: Maize, rice, and cotton.
   • Cool crops: Wheat, barley, and cabbage.

6. Rainfall:

   • Rain-fed crops (e.g., rice) thrive in high rainfall areas.
   • Drought-resistant crops (e.g., millets) perform well in low-rainfall zones.

7. Geographical Factors:

   • Altitude, latitude, and local climate conditions.
   • Example: Coffee grows well in high altitudes, while coconut thrives in coastal regions.

How to Update Recommendations

1. Dynamic Soil Profiles:

   • Use real-time soil testing data to determine nutrient levels, pH, and moisture.
   • Example: If the nitrogen level is low, recommend nitrogen-fixing crops like legumes.

2. Crop Rotation Insights:

   • Suggest crop rotations to maintain soil health.
   • Example: After a nitrogen-depleting crop like wheat, recommend a nitrogen-fixing crop like lentils.

3. Fertilizer Suggestions:

   • Provide recommendations for fertilizers based on deficiencies.
   • Example: If phosphorus is low, suggest adding rock phosphate.

4. Weather and Climate Integration:

   • Include real-time weather data like rainfall forecasts and temperature trends.
   • Example: Recommend drought-tolerant crops during dry seasons.

5. Regional Crop Suitability:

   • Use regional data to match crops with local soil and climate.
   • Example: Recommend paddy in water-rich regions like Punjab, and millet in arid regions like Rajasthan.

Sample Output for Crop Recommendations

Based on soil and environmental data: - Soil Parameters: - pH: 6.8 (neutral) - Nitrogen: Medium - Phosphorus: Low - Potassium: High - Moisture: Moderate - Recommendations: - Primary Crops: Wheat, Maize, Barley. - Secondary Crops (Improving Soil Health): Lentils, Chickpeas (for nitrogen fixation). - Fertilizer Recommendation: Use phosphorus-rich fertilizers (e.g., DAP).

How to Present Recommendations

• Use a dashboard or mobile app for farmers.
• Show clear visualizations of soil test results and matched crops.
• Include:
  • Top recommended crops.
  • Fertilizer and irrigation tips. -``

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back extension) and from 1979 onwards (final release plus timely updates, this page). ERA5 replaces the ERA-Interim reanalysis.

Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.

ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread.

ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. So far this has not been the case and when this does occur users will be notified.

The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications.

An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines.

Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).

The present entry is "ERA5 monthly mean data on single levels from 1979 to present".