Medium- and Extended-Range Ensemble Weather Forecasting

Richardson, David

doi:10.1007/978-3-319-68418-5_8

Cited by 3 publications

(4 citation statements)

References 33 publications

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“…At these timescales the influence of initial atmospheric conditions, used to initialise the model, reduces and chaotic processes in the Earth system become more dominant. These timescales are considered the limit for day-to-day predictability [39]. Forecasts are also available for extended-and long-range (46 days and 3-7 months ahead, respectively) associated with longer-term predictability in the climate system.…”

Section: Model Forecasts and Projectionsmentioning

confidence: 99%

“…Subseasonal, seasonal to decadal forecasts make use of important long-term sources of predictability within the Earth system, e.g. the Madden-Julian Oscillation or El-Niño Southern Oscillation, which provide potential for forecasts from 1 month up to 1 decade ahead [39,[41][42][43]. Although these forecasts are generally more uncertain than weather forecasts, for biosecurity applications there are many opportunities to utilise these as input to pest, pathogen and disease EWSs [44], or for strategic agricultural decisions, e.g.…”

Section: Model Forecasts and Projectionsmentioning

confidence: 99%

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Use of meteorological data in biosecurity

Hemming

Macneill

2020

Emerging Topics in Life Sciences

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Pests, pathogens and diseases cause some of the most widespread and damaging impacts worldwide — threatening lives and leading to severe disruption to economic, environmental and social systems. The overarching goal of biosecurity is to protect the health and security of plants and animals (including humans) and the wider environment from these threats. As nearly all living organisms and biological systems are sensitive to weather and climate, meteorological, ‘met’, data are used extensively in biosecurity. Typical applications include, (i) bioclimatic modelling to understand and predict organism distributions and responses, (ii) risk assessment to estimate the probability of events and horizon scan for future potential risks, and (iii) early warning systems to support outbreak management. Given the vast array of available met data types and sources, selecting which data is most effective for each of these applications can be challenging. Here we provide an overview of the different types of met data available and highlight their use in a wide range of biosecurity studies and applications. We argue that there are many synergies between meteorology and biosecurity, and these provide opportunities for more widespread integration and collaboration across the disciplines. To help communicate typical uses of meteorological data in biosecurity to a wide audience we have designed the ‘Meteorology for biosecurity’ infographic.

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Section: Model Forecasts and Projectionsmentioning

confidence: 99%

Section: Model Forecasts and Projectionsmentioning

confidence: 99%

Use of meteorological data in biosecurity

Hemming

Macneill

2020

Emerging Topics in Life Sciences

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“…Thus, the season-long outlooks might fail to capture the variances and occurrences of extreme events at daily time step [13,14], hugely limiting the applicability in crop growth models. The spatio-temporal uncertainties in the simulated weather parameters during season-long predictions can be reduced with medium-range forecasts [15]. Ortiz-Monasterio et al [3], after analysing winter wheat from 1985-1992 in the district of Punjab, India, suggested that high temperatures about and after the heading date (which occurred during the middle of February) had a detrimental effect on the crop yield.…”

Section: Introductionmentioning

confidence: 99%

In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model

Kirthiga

Patel

2022

AgriEngineering

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In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha−1 and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability.

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“…A deterministic forecast gives no indication of the forecast sensitivity to initial errors and approximations: How likely is a given forecast to be correct? Ensemble prediction is becoming increasingly important on all timescales from the short (one or three days ahead) to the seasonal range and beyond (Ancell, 2013; Richardson, 2018; Palmer, 2019). An ensemble prediction system (EPS) is an NWP system in which several predicted scenarios from the same model are used to represent the uncertainty in a weather forecast as well as the most likely outcome.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the August 2018 heavy rainfall events over Kerala with high‐resolution NWP models

Ashrit

Sharma

Kumar

et al. 2020

Meteorological Applications

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The southern Indian state of Kerala experienced exceptionally high rainfall during August 2018, which led to devastating floods in many parts of the state. Prediction and early warning of severe weather events in vulnerable areas is crucial for disaster management agencies in order to protect life and property. In recent years, state-of-the-art numerical weather prediction (NWP) models have been used operationally to predict rainfall over different spatial and temporal scales. In the present paper, predictions based on the National Centre for Medium Range Weather Forecasting (NCMRWF) models (NCUM, NCUM-R and NEPS) are assessed over Kerala to demonstrate the capabilities of highresolution models. It is found that the deterministic NWP model (NCUM and NCUM-R) forecasts are accurate at shorter lead times (up to Day 3) mainly in terms of timing and, to some extent, intensity. At higher lead times (beyond Day 3), the ensemble-based probabilistic forecasts are useful and actionable. K E Y W O R D S ensemble model, extreme rainfall, probabilistic forecasts

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Medium- and Extended-Range Ensemble Weather Forecasting

Cited by 3 publications

References 33 publications

Use of meteorological data in biosecurity

Use of meteorological data in biosecurity

In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model

Prediction of the August 2018 heavy rainfall events over Kerala with high‐resolution NWP models

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