The Climate Corporation

Holly Dail

Alex Kleeman

Patricia Ann Carroll

Bethany Susan Porter Grabow

Jerald Keith Pataky

Ana Malagon

Stephan Hoyer

Systems and methods for determining a risk of damage to a crop on a field are described. In an example embodiment, a method for limiting such damage to a crop includes receiving, for multiple hours, weather data identifying temperature values and humidity values for a geographic location of the field, determining, for the multiple hours, that a temperature value is within a first range of values and a humidity value is within a second range of values and, identifying each of the multiple hours as a risk hour for a disease. The method also includes computing a risk value for the field based on the identified risk hours, determining that the risk value is above a threshold, and determining that the crop on the field is at risk for the disease. The method then includes spraying the crop on the field with a damage mitigating chemical specific to the disease.

Computing risk from a crop damaging factor for a crop on an agronomic field

Systems and methods for determining a risk of damage to a crop on an agronomic field are described. In an embodiment, a computer system receives, for each hour of a first day, weather data identifying temperature values and humidity values associated with a geographic location. The computer system determines, for a particular hour of the first day, that a temperature value is within a first range of values and a humidity value is within a second range of values and, in response, identifies the particular hour as a risk hour. The computer system computes, for a second day, a risk value for one or more agronomic fields at the geographic location based, at least in part, on one or more identified risk hours between a day of planting a crop on the one or more agronomic fields and the second day. The computer system determines that the risk value is above a risk value threshold and, in response, determines that the crop on the one or more agronomic fields is at risk of suffering damage from a particular crop damaging factor. The computer system stores data indicating that the crop is at risk of suffering damage from the particular crop damaging factor.

In an approach, a computer receives an observation dataset that identifies one or more ground truth values of an environmental variable at one or more times and a reforecast dataset that identifies one or more predicted values of the environmental variable produced by a forecast model that correspond to the one or more times. The computer then trains a climatology on the observation dataset to generate an observed climatology and trains the climatology on the reforecast dataset to generate a forecast climatology. The computer identifies observed anomalies by subtracting the observed climatology from the observation dataset and forecast anomalies by subtracting the forecast climatology from the reforecast dataset. The computer then models the observed anomalies as a function of the forecast anomalies, resulting in a calibration function, which the computer can then use to calibrate new forecasts received from the forecast model.

Long-range temperature forecasting

A method for calibrating forecasts involving temperature, precipitation, and other weather related variables is provided. In an embodiment historical ensemble-based forecasts and historical observations are received by an agricultural intelligence computing system. Historical differences are determined between the forecasts and the observations corresponding to the forecasts and stored in the volatile memory of the agricultural intelligence computing system. The agricultural intelligence computing system receives current ensemble-based forecasts and a request for improved forecasts. The agricultural intelligence computing system retrieves the historical differences and uses a combination of the historical differences and the current ensemble-based forecasts to create probability distributions for the weather for each lead day. The agricultural intelligence computing system then samples from the probability distributions to create improved ensemble-based forecasts at the requested location.

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Seattle, WA, United States of America

Holly Dail