Short-term predictions of hydrometeorological variables such as wind, precipitation, temperature, humidity, pressure, and runoff from 1 to 15 days can be provided up to 1 km spatial resolution over any location.
Seasonal forecasts of hydrometeorological variables such as wind, precipitation, temperature, humidity, pressure, and runoff from 1 to 6 months can be provided over any location.
Long-term projections of hydrometeorological variables such as wind, precipitation, temperature, humidity, pressure, and runoff from 50 to 100 years can be provided over any location.
Wind and Solar Energy Power Potential Prediction
We can provide short term high accuracy wind speed forecasts for the wind turbines so that forecasts can be utilized in the wind power generation related applications.
Hydropower Potential Prediction
We can provide short-term and seasonal runoff forecasts and calculate the runoff potential generated by the basin of reservoirs. Such predictions are particularly useful for the decision makers designing early warning systems as well as generating hydropower energy.
Drought Analysis and Risk Assessment
We can analyze and make seasonal forecasts and climate projections of the drought characteristics (magnitude, duration, and frequency) over regions. Such assessments done in space and time for different drought types (meteorological, hydrological, agricultural) are crucial for drought risk related applications.
Forest Fire Risk Assessment
We can assess and predict the forest fire risk of regions by using remote sensing observations and model simulations.
Land Cover Classification
We can classify the change and detect the existing conditions (e.g., assessment of the spread of forest fires, urban land area, water body) of different land covers.
Agricultural Crop Classification
We can detect the agricultural crops over field scale to large regions. Such applications are also useful for analysis of the spatial and the temporal variability of grown agricultural crops.
Crop Yield Prediction
We can provide predictions of the yields of crops several months in advance utilizing remote sensing observations and model simulations supported by machine learning algorithms.
Big Data Analysis
We can detect spatial and/or temporal signals existing in time series or spatially extensive datasets by utilizing various artificial intelligence and statistical techniques. The relevant information that is hidden in the big datasets can be mined at high precision.