AgriMetSoft

Taylor Diagram Software

Taylor diagrams offer a comprehensive way to evaluate and compare models based on multiple metrics at once. By visually comparing the positions of models on the Taylor diagram, their performance in matching observations can be assessed. Models closer to the reference point indicate better agreement with the observed data.

Open NC File

The Open NC File tool is a versatile tool for extracting time series data from NetCDF files without limitations. It can read any NetCDF file, including rotated-pole grids like CORDEX data, in nc or nc4 formats. Users can extract data based on a list of station coordinates or rectangular regions. Multiple files can be temporarily merged by the tool, which supports unlimited dimensions. The tool automatically converts regular latitude and longitude to rotated ones. Overall, Open NC File provides powerful features for flexible data extraction from several NetCDF files at once.

SD-GCM

In this package, users can utilize three statistical downscaling methods on any CMIP5/CMIP6 models or gridded data. To begin, you will need to download historical and RCPs/SSPs NetCDF files from ESGF or other relevant websites. Additionally, ensure that you have access to station data. With these resources in hand, the tool can perform statistical downscaling using three methods: Delta, Quantile Mapping, and Empirical Quantile Mapping.

Cordex Coordinate Rotation

This tool provides the capability to convert coordinates between non-rotated and rotated formats. The rotation pivot is based on CORDEX domains and can be customized as per your requirements. To extract data from CORDEX NetCDF files, you are required to convert your regular coordinates to rotated coordinates using this tool. Once the conversion is complete, you can proceed with data extraction using the rotated coordinates and rlat/rlon variables available in your file. Data extraction can be accomplished using any programming language or by utilizing the Netcdf Extractor provided on this website.

IDF Curve Tool

The IDF curve tool offers various distributions for generating IDF (Intensity-Duration-Frequency) charts. There are two options available for inputting data. In the first tab, you can input your raw data at a scale ranging from 5 minutes to daily, and the IDF tool will calculate the corresponding maximum values in the second tab. Alternatively, you have the option to directly input the maximum values in the second tab instead of raw data. In the third tab, you can generate IDF graphs for specific return periods. A return period represents the average time or estimated average time between events, such as the occurrence of rainfall intensities. The tool allows you to save the IDF data in an Excel file and draw the IDF graphs within Excel.

DMAP

The DMAP software is equipped with advanced capabilities to calculate a diverse range of drought indices for three distinct types of drought: meteorological, agricultural, and hydrological. It offers a user-friendly feature that allows easy input of station data through Excel files. Furthermore, the software supports the utilization of NetCDF data from stations, rectangular regions, or shapefiles, providing flexibility in data sources. By utilizing this powerful tool, you can accurately evaluate and assess drought severity. Additionally, the software allows you to export all the results to an Excel file. With DMAP, you can conveniently calculate indices such as SPI, DI, PNI, CZI, MCZI, ZSI, RAI, EDI, PDSI, PHDI, RDI, SPEI, KBDI, ARI, SMDI, ETDI, SWSI, SDI.

Data-Tool

In summary, Data-Tool serves as an Excel add-in that empowers users to efficiently manipulate and analyze their data. With features like data reshaping, sorting, and the computation of various indices, it enables users to gain valuable insights from their data. Data-Tool is a powerful Excel add-in that simplifies data sorting and reshaping, enabling users to easily apply various indices to their data. To calculate these essential indices using Data-Tool, it is necessary to format the data in a specific manner that allows for seamless analysis. This Excel add-in provides a range of functionalities, including data reshaping and sorting, conversion of daily data to monthly or seasonal formats, and the computation of efficiency criteria such as Root Mean Square Error, Nash Sutcliffe model Efficiency coefficient, Mean Absolute Error, and more. Data-Tool also offers additional capabilities, such as generating CDF/PDF charts, performing multiple linear regression (MLR), and conducting Trend tests. Furthermore, we continuously enhance the tool by adding new functionalities to further support diverse data analysis needs.

KNN-WG

The KNN Weather Generator is a powerful tool that enables users to simulate daily weather data using the K-nearest-neighbor approach. With this software, users can conveniently load seven different variables, including Tmin, Tmax, Rain, Srad, ETo, WSPD, and Humidity. This software offers various features to enhance the user experience. Users can visualize the generated data through interactive graphs and also calculate several efficiency criteria, such as d, NSE, RMSE, MBE, Pearson, and Spearman. Moreover, the KNN Weather Generator allows users to compare its outputs with results from other models. By importing the outputs of different models, users can make comprehensive comparisons and evaluate the effectiveness of the KNN Weather Generator in generating accurate weather data. One of the notable advantages of the KNN Weather Generator software is its flexibility in variable selection. Users have the freedom to choose any number of variables they desire, starting from a minimum of seven variables. Its range of features, including variable selection, graph visualization, efficiency criteria calculation, and result comparison, make it an invaluable resource for weather prediction and analysis.

MODIS Product Extractor

The "MODIS Product Extractor" is a specialized tool designed to extract data from MODIS HDF files of various products. This tool offers several features that facilitate data extraction based on specific requirements. With the MODIS Product Extractor, you can perform the following actions: Extract data from points or regions: The tool allows you to extract data from specific points or a list of points defined by their coordinates. Additionally, you can extract data from rectangular regions by specifying the corner coordinates. Furthermore, you have the flexibility to select a region of any shape on a map and extract data for that specific area. Export to Excel: The extracted data can be exported to Excel for further analysis or integration with other tools. This export functionality enables you to utilize the extracted data in a tabular format that is compatible with Excel or other spreadsheet software. The MODIS Product Extractor simplifies the process of extracting data from MODIS HDF files by providing a user-friendly interface and efficient extraction capabilities. It streamlines the workflow for accessing specific data points or regions of interest and offers the convenience of exporting the extracted data to Excel for further analysis or reporting.

Ensemble GCMs Models

Ensemble Global Climate Models (GCMs) refer to a collection of multiple climate models employed to simulate and forecast the behavior of Earth's climate system. This powerful tool involves combining several GCM models using various methods, allowing for a comprehensive assessment of climate predictions and associated uncertainties. By employing ensembles, scientists can gain insights into the range of potential outcomes and improve the reliability of climate projections.

Estimating Missing Climate Data

Estimating missing climate data is a crucial process in climate research and analysis, especially when certain data points or periods are unavailable or incomplete due to sensor malfunctions, data transmission issues, or gaps in historical records. To address these gaps, four methods are commonly employed:

• Multiple Linear Regression (MLR)
• Correlation Coefficient Weighting
• Inverse Distance Weighting (IDW)
• Artificial Neural Networks (ANN)

Wavelet ANN Model

The Wavelet Artificial Neural Network (Wavelet ANN) model is a hybrid approach that combines the mathematical framework of wavelet analysis with the powerful learning capabilities of artificial neural networks (ANNs). This model is commonly used for various types of data analysis and forecasting tasks, including time series prediction, image processing, and signal analysis. Our software includes two hybrid models of Wavelet and ANN based on the above flowcharts.

Non-Stationary IDF

Non-stationary Intensity-Duration-Frequency (IDF) analysis is a concept in hydrology and climatology that takes into account changes in the statistical properties of extreme precipitation events over time. The traditional approach for estimating IDF curves assumes that the statistical characteristics of extreme rainfall, such as intensity, duration, and frequency, remain constant over time. However, with climate change and other environmental factors affecting weather patterns, this assumption of stationarity may no longer hold true. This tool is designed to perform Non-stationary Intensity-Duration-Frequency (IDF) analysis by considering changes in extreme precipitation characteristics over time.

SCS-Runoff-Curve-Number-Method

The Soil Conservation Service (SCS) Runoff Curve Number (RCN) Method, also known as the NRCS (Natural Resources Conservation Service) Curve Number Method, is a widely used hydrological technique for estimating direct runoff from rainfall events in a watershed. The method was developed by the United States Department of Agriculture's Soil Conservation Service, now known as the Natural Resources Conservation Service, and is commonly applied in hydrology, engineering, and land management practices. This is a simple tool to calculate estimating direct runoff from rainfall events in a watershed based on SCS-Runoff-Curve-Number-Method

Trend Test Software

In this tool, you can input monthly climate data, and the tool will convert it into seasonal, season12, discrete all season12 items, and yearly data. The next step is to check the autocorrelation with a one-item lag to determine which method is suitable. The tool offers four methods to calculate the data trend on all time scales and wavelet components (approximation and details, and their composition). These methods include:

• Simple Mann-Kendall
• Seasonal Mann-Kendall
• Serial Correlation Mann-Kendall
• Sequential Mann-Kendall

Using these methods, the tool helps you analyze trends and patterns in your climate data at various time scales and wavelet components, making it a powerful tool.

Weather Data Sorter

These tools calculate the indices that offer valuable insights into various aspects of climate and weather conditions and are commonly used by meteorologists, hydrologists, and climate scientists for analyzing climate patterns and understanding potential impacts on various sectors like agriculture, water resources, and energy demand. These indices are climate-related measures commonly used in climate analysis and impact studies. Each index represents a specific aspect of the climate or weather conditions. These indices is:

• Growing Degree Days (GDD)
• Cooling Degree Days (CDD)
• Heating Degree Days (HDD)
• Wet Day
• Number of Frost Days (N. Frost Day)
• Consecutive Dry Days
• Consecutive Wet Days
• Annual Count of Rainy Days
• Annual Count of Days With Precipitation Larger Than Value
• Highest 5-day Precipitation Amount for Each Year
• Simple Precipitation Intensity Index

Thiessen Polygon

Thiessen polygons, also known as Voronoi polygons or Thiessen regions, are a method used in geography, cartography, and spatial analysis to divide an area into regions based on proximity to specific points or geographic features. In the context of averaging rainfall data from multiple stations, the Thiessen polygon method is a technique used to distribute the rainfall measurements spatially across a given area based on the proximity of each rain gauge station. This tool will calculate the area of each station to find the average rainfall of a region.

Power-spectral-density (PSD) Analysis

In climate science, Power Spectral Density (PSD) analysis is a method used to study the frequency characteristics of climate time series data. It is a valuable tool for understanding the dominant frequencies and periodicities present in climatic data, which can provide insights into the underlying climate processes and variability.