Numerical Weather Prediction Forecasts Research Articles

A Novel Methodology of Stochastic Short Term Forecasting of Cloud Boundaries

Following the chaos theory proposed by Lorenz, probabilistic approaches have been widely used in numerical weather prediction. This paper introduces a convection diffusion equation to quantify the dynamic growth of uncertainty for short term forecasts of cloud boundaries. The equation is inserted into a numerical weather prediction model, weather research and forecast. A two parameter model based on wind velocity dispersion and surface evaporation rates parameterizes the stochastically motivated, but deterministic, equation. Prediction verification tests in comparison to observed data show good predictive capability for an hour, with a gradual loss of predictive power continuing for predictions up to three hours shown qualitatively. The methodology can be applied to a variety of topics in numerical weather prediction research.

Critical weather situations for renewable energies – Part B: Low stratus risk for solar power

Accurately predicting the formation, development and dissipation of fog and low stratus (LS) still poses a challenge for numerical weather prediction (NWP) models. Errors in the low cloud cover NWP forecasts directly impact the quality of photovoltaic (PV) power prediction. On days with LS, day-ahead forecast errors of Germany-wide PV power frequently lie within the magnitude of the balance energy and thus pose a challenge for maintaining grid stability. An indication in advance about the possible occurrence of a critical weather situation such as LS would represent a helpful tool for transmission system operators (TSOs) in their day-to-day business. In the following, a detection algorithm for low stratus risk (LSR) is developed and applied as post-processing to the NWP model forecasts of the regional non-hydrostatic model COSMO-DE, operational at the German Weather Service. The aim of the LSR product is to supply day-ahead warnings and to support the decision making process of the TSOs. The quality of the LSR is assessed by comparing the computed regions of LSR occurrence with a satellite based cloud classification product from the Nowcasting Satellite Facility (NWCSAF). The results show that the LSR provides additional information that should in particular be useful for risk adverse users.

Radiation fog formation alerts using attenuated backscatter power from automatic lidars and ceilometers

Abstract. Radiation fog occurs over many locations around the world in stable atmospheric conditions. Air traffic at busy airports can be significantly disrupted because low visibility at the ground makes it unsafe to take off, land and taxi on the ground. Current numerical weather prediction forecasts are able to predict general conditions favorable for fog formation, but not the exact time or location of fog occurrence. A selected set of observations available in near-real time at strategic locations could also be useful to track the evolution of key processes and key parameters that drive fog formation. Such observations could complement the information predicted by numerical weather prediction (NWP) models that is made available to airport forecasters in support of their fog forecast. This paper presents an experimental setup based on collocated automatic lidar and ceilometer measurements, relative humidity measurements and horizontal visibility measurements to study hygroscopic growth of fog condensation nuclei. This process can take several minutes to hours, and can be tracked using lidar- or ceilometer-attenuated backscatter profiles. Based on hygroscopic growth laws we derive a set of parameters that can be used to provide alerts minutes to hours prior to formation of radiation fog. We present an algorithm that uses the temporal evolution of attenuated backscatter measurements to derive pre-fog formation alerts. The performance of the algorithm is tested on 45 independent pre-fog situations at two locations (near Paris, France, and Brussels, Belgium). We find that an alert for pre-fog conditions predominantly occurs 10–50 min prior to fog formation at an altitude ranging 0 to 100 m above ground. In a few cases, alerts can occur up to 100 min prior to fog formation. Alert durations are found to be sensitive to the relative humidity conditions found a few hours prior to the fog.

Nowcasting of deep convective clouds and heavy precipitation: Comparison study between NWP model simulation and extrapolation

An evaluation of convective cloud forecasts performed with the numerical weather prediction (NWP) model COSMO and extrapolation of cloud fields is presented using observed data derived from the geostationary satellite Meteosat Second Generation (MSG). The present study focuses on the nowcasting range (1–5h) for five severe convective storms in their developing stage that occurred during the warm season in the years 2012–2013. Radar reflectivity and extrapolated radar reflectivity data were assimilated for at least 6h depending on the time of occurrence of convection. Synthetic satellite imageries were calculated using radiative transfer model RTTOV v10.2, which was implemented into the COSMO model. NWP model simulations of IR10.8μm and WV06.2μm brightness temperatures (BTs) with a horizontal resolution of 2.8km were interpolated into the satellite projection and objectively verified against observations using Root Mean Square Error (RMSE), correlation coefficient (CORR) and Fractions Skill Score (FSS) values. Naturally, the extrapolation of cloud fields yielded an approximately 25% lower RMSE, 20% higher CORR and 15% higher FSS at the beginning of the second forecasted hour compared to the NWP model forecasts. On the other hand, comparable scores were observed for the third hour, whereas the NWP forecasts outperformed the extrapolation by 10% for RMSE, 15% for CORR and up to 15% for FSS during the fourth forecasted hour and 15% for RMSE, 27% for CORR and up to 15% for FSS during the fifth forecasted hour. The analysis was completed by a verification of the precipitation forecasts yielding approximately 8% higher RMSE, 15% higher CORR and up to 45% higher FSS when the NWP model simulation is used compared to the extrapolation for the first hour. Both the methods yielded unsatisfactory level of precipitation forecast accuracy from the fourth forecasted hour onward.

Feature-Based Diagnostic Evaluation of Global NWP Forecasts

AbstractWith the resolution of global numerical weather prediction (NWP) models now typically between 10 and 20 km, forecasts are able to capture the evolution of synoptic features that are important drivers for significant surface weather. The position, timing, and intensity of jet cores, surface highs and lows, and changes in the behavior of these forecast features is explored using the Method for Object-based Diagnostic Evaluation (MODE) at the global scale. Previously this was only possible with a more subjective approach. The spatial aspects of the forecast features (objects) and their intensity can be assessed separately. The evolution of paired forecast–analysis object attributes such as location and orientation differences, as well as area ratios, can be considered. The differences in the paired object attribute distributions from various model configurations were evaluated using the k-sample Anderson–Darling (AD) test. Increases or decreases in hits, false alarms (forecast-not-observed), and misses (observed-not-forecast) features were also assessed. It was found that when focusing purely on the forecast features of interest, differences in seasonal spatial extent biases emerged, intensity biases varied as a function of analysis time, and changes in the attribute distributions could be detected but were largely insignificant, primarily due to sample size. As has been shown for kilometer-scale NWP, results from spatial verification methods are more in line with subjective assessment. This type of objective assessment provides a new dimension to the traditional assessment of global NWP, and provides output that is closer to the way in which forecasts are used.

On the Impact and Benefits of AMDAR Observations in Operational Forecasting—Part I: A Review of the Impact of Automated Aircraft Wind and Temperature Reports

Abstract This paper reviews the impact of World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) observations on operational numerical weather prediction (NWP) forecasts at both regional and global scales that support national and local weather forecast offices across the globe. Over the past three decades, data collected from commercial aircraft have helped reduce flight-level wind and temperature forecast errors by nearly 50%. Improvements are largest in 3–48-h forecasts and in regions where the automated reports 1) are most numerous, 2) cover a broad area, and 3) are available at multiple levels (e.g., made during aircraft ascent and descent). Improvements in weather forecasts due to these data have already had major impacts on a variety of aspects of airline operations, ranging from fuel savings from improved wind and temperature forecasts used in flight planning to passenger comfort and safety due to better awareness of en route and near-terminal weather hazards. Aircraft wind and temperature observations now constitute the third most important dataset for global NWP and, in areas of ample reports, have become the single most important dataset for use in shorter-term, regional NWP applications. Automated aircraft reports provide the most cost-effective data source for improving NWP, being more than 5 times more cost effective than any other major-impact observing system. They also present an economical alternative for obtaining tropospheric profiles both in areas of diminishing conventional observation and as a supplement to existing datasets, both in time and space. An evaluation of moisture observations becoming available from an increasing number of AMDAR-equipped aircraft will be presented in Part II of this paper, including examples of the use of the full array of AMDAR observations in a variety of forecasting situations.

On the assimilation of satellite sounder data in cloudy skies in numerical weather prediction models

Satellite measurements are an important source of global observations in support of numerical weather prediction (NWP). The assimilation of satellite radiances under clear skies has greatly improved NWP forecast scores. However, the application of radiances in cloudy skies remains a significant challenge. In order to better assimilate radiances in cloudy skies, it is very important to detect any clear field-of-view (FOV) accurately and assimilate cloudy radiances appropriately. Research progress on both clear FOV detection methodologies and cloudy radiance assimilation techniques are reviewed in this paper. Overview on approaches being implemented in the operational centers and studied by the satellite data assimilation research community is presented. Challenges and future directions for satellite sounder radiance assimilation in cloudy skies in NWP models are also discussed.

Development and validation of satellite-based estimates of surface visibility

Abstract. A satellite-based surface visibility retrieval has been developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a proxy for Advanced Baseline Imager (ABI) data from the next generation of Geostationary Operational Environmental Satellites (GOES-R). The retrieval uses a multiple linear regression approach to relate satellite aerosol optical depth, fog/low cloud probability and thickness retrievals, and meteorological variables from numerical weather prediction forecasts to National Weather Service Automated Surface Observing System (ASOS) surface visibility measurements. Validation using independent ASOS measurements shows that the GOES-R ABI surface visibility retrieval (V) has an overall success rate of 64.5 % for classifying clear (V ≥ 30 km), moderate (10 km ≤ V < 30 km), low (2 km ≤ V < 10 km), and poor (V < 2 km) visibilities and shows the most skill during June through September, when Heidke skill scores are between 0.2 and 0.4. We demonstrate that the aerosol (clear-sky) component of the GOES-R ABI visibility retrieval can be used to augment measurements from the United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires.

Estimating background error covariance parameters and assessing their impact in the OSTIA system

Abstract To generate sea surface temperature (SST) analyses, observations are assimilated onto a background field to produce a gap-free map of SST. The spatial influence of an observation as well as its weight are determined by the error covariance matrices used in the data assimilation. The background error covariance parameters for an SST analysis have been estimated using observation-minus-background difference data from a long term SST reanalysis. Geographically varying estimates were obtained of both the error variance and the associated length scale of the background error which were decomposed into small-scale and large-scale errors. Both components of the background error variances were found to be seasonally variable. Minimal seasonality was observed in the error correlation length scales but they were found to be anisotropic. Observational errors were also estimated for the observation types used in the observation-minus-background calculations. The impact of updating the background error parameters in the Operational Sea surface Temperature and sea-Ice Analysis (OSTIA) was assessed. The updates improved the analysis accuracy compared to assimilated and independent reference observations. The covariance updates also improved the ability of the SST analysis to represent small-scale ocean features without introducing spurious noise. As OSTIA is used as boundary conditions in the numerical weather prediction (NWP) system at the Met Office the impact on the NWP forecasts was also assessed. The OSTIA changes were found to have a positive impact on NWP forecast skill.

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Solar radiation is a key factor in the Earth’s energy balance and it is used as a crucial input parameter in many disciplines such as ecology, agriculture, solar energy and hydrology. Thus, accurate information of the global downward surface shortwave flux integration into the grid is of significant importance. From the different strategies used for grid integration of the surface solar radiation estimates, satellite-derived and numerical weather prediction forecasts are two interesting alternatives. In the current work, we present a comprehensive evaluation of the global downward solar radiation forecasts provided by the Regional Atmospheric Modeling System (RAMS) and the Downwelling Surface Shortwave Flux (DSSF) product, derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Both solar radiation estimates are compared to thirteen ground-based weather station measurements for the winter 2010–2011 and the summer 2011 seasons. For these periods, the most recent versions of RAMS (4.4 and 6.0) were running in parallel within the real-time weather forecasting system implemented over the Valencia Region. The solar radiation performance and accuracy are evaluated for these datasets segmented into two atmospheric conditions (clear and cloudy skies) and two terrain classes (flat and hilly). DSSF shows a very good agreement over the study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging from −30 to 40 W·m−2, absolute root mean square errors (RMSE) from 25 to 60 W·m−2, relative bias ranging from −11% to 7% and relative RMSE from 7% to 22%, depending on the sky condition and the terrain location as well, thus reproducing the observations more faithfully than RAMS, which produces higher errors in comparison to the measurements. In this regard, statistical daily evaluations show absolute bias values varying from −50 to 160 W·m−2, absolute RMSE from 60 to 240 W·m−2, relative bias ranging from −30% to 40% and relative RMSE from 10% to 80%, also depending on the daily initialization and the forecast horizon. This bias variability demonstrates that there is a different trend in the deviation of the model results in relation to the observations, both for the DSSF product and RAMS forecasts, and considering the summer and the winter seasons independently. In this regard, although there is an overestimation of the observed solar radiation within the summer months, this magnitude is underestimated during the winter. Finally, comparing this solar radiation estimates for different atmospheric conditions and different terrain classes, the best results are found under clear skies over flat terrain. This result is achieved using both methodologies.

Microwave hyperspectral measurements for temperature and humidity atmospheric profiling from satellite: The clear‐sky case

AbstractThis study investigates the benefits of a satellite HYperspectral Microwave Sensor (HYMS) for the retrieval of atmospheric temperature and humidity profiles, in the context of numerical weather prediction (NWP). In the infrared, hyperspectral instruments have already improved the accuracy of NWP forecasts. Microwave instruments so far only provide observations for a limited number of carefully selected channels. An information content analysis is conducted here to assess the impact of hyperspectral microwave measurements on the retrieval of temperature and water vapor profiles under clear‐sky conditions. It uses radiative transfer simulations over a large variety of atmospheric situations. It accounts for realistic observation (instrument and radiative transfer) noise and for a priori information assumptions compatible with NWP practices. The estimated retrieval performance of the HYMS instrument is compared to those of the microwave instruments to be deployed on board the future generation of European operational meteorological satellites (MetOp‐SG). The results confirm the positive impact of a HYMS instrument on the atmospheric profiling capabilities compared to MetOp‐SG. Temperature retrieval uncertainty, compared to a priori information, is reduced by 2 to 10%, depending on the atmospheric height, and improvement rates are much higher than what will be obtained with MetOp‐SG. For humidity sounding these improvements can reach 30%, a significant benefit as compared to MetOp‐SG results especially below 250 hPa. The results are not very sensitive to the instrument noise, under our assumptions. The main impact provided by the hyperspectral information originates from the higher resolution in the O2 band around 60 GHz. The results are presented over ocean at nadir, but similar conclusions are obtained for other incidence angles and over land.

Asymmetric Features of Oceanic Microwave Brightness Temperature in HighSurface Wind Speed Condition

Asymmetric features of oceanic brightness temperature from spaceborne microwave imagers in high surface wind speed conditions were investigated with two kinds of collocated data. The first is simultaneous measurements of microwave brightness temperatures and surface wind vectors from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds on Advanced Earth Observing Satellite II. The second is microwave brightness temperature observations (AMSR2 and the Special Sensor Microwave Imager Sounder) and surface wind vectors in the European Centre for Medium-Range Weather Forecasts numerical weather prediction model. Both collocated data sets showed that the vertical-polarized and the horizontal-polarized microwave brightness temperature have out-of-phase asymmetric features in terms of relative wind direction (RWD) at high surface wind speeds. Furthermore, different asymmetric features were found for the northern and Southern Hemispheres and for ascending and descending satellite orbits. Although similar asymmetric features can be found in other microwave imager studies, the causes of the asymmetry have not been fully investigated. To investigate the cause of the asymmetry, the observation frequency regarding air–sea temperature difference was examined in upwind, downwind, and crosswind cases. Two important factors contribute to the asymmetry. First, the observations from inclined polar orbit satellites provide different samplings on atmospheric stability in terms of the RWD. Second, the oceanic microwave brightness temperatures have negative correlations with atmospheric stability at high surface wind speeds. The out-of-phase asymmetry is closely related with atmospheric stability, and it appears under a high-surface-wind-speed condition.

Inherent Bounds on Forecast Accuracy due to Observation Uncertainty Caused by Temporal Sampling

Abstract Synoptic observations are often treated as error-free representations of the true state of the real world. For example, when observations are used to verify numerical weather prediction (NWP) forecasts, forecast–observation differences (the total error) are often entirely attributed to forecast inaccuracy. Such simplification is no longer justifiable for short-lead forecasts made with increasingly accurate higher-resolution models. For example, at least 25% of t + 6 h individual Met Office site-specific (postprocessed) temperature forecasts now typically have total errors of less than 0.2 K, which are comparable to typical instrument measurement errors of around 0.1 K. In addition to instrument errors, uncertainty is introduced by measurements not being taken concurrently with the forecasts. For example, synoptic temperature observations in the United Kingdom are typically taken 10 min before the hour, whereas forecasts are generally extracted as instantaneous values on the hour. This study develops a simple yet robust statistical modeling procedure for assessing how serially correlated subhourly variations limit the forecast accuracy that can be achieved. The methodology is demonstrated by application to synoptic temperature observations sampled every minute at several locations around the United Kingdom. Results show that subhourly variations lead to sizeable forecast errors of 0.16–0.44 K for observations taken 10 min before the forecast issue time. The magnitude of this error depends on spatial location and the annual cycle, with the greater errors occurring in the warmer seasons and at inland sites. This important source of uncertainty consists of a bias due to the diurnal cycle, plus irreducible uncertainty due to unpredictable subhourly variations that fundamentally limit forecast accuracy.

An analog ensemble for short-term probabilistic solar power forecast

The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the solar elevation and the solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the solar radiation within the troposphere is the main meteorological factor influencing the solar power production, as a fraction of short wave solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on solar photovoltaic power prediction. This lack of predictability makes the solar energy integration into the grid challenging. A cost-effective utilization of solar energy over a grid strongly depends on the accuracy and reliability of the power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring solar power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic solar power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model forecasts and observations of the solar power. For each forecast lead time and location, the ensemble prediction of solar power is constituted by a set of past production data. These measurements are those concurrent to past deterministic NWP forecasts for the same lead time and location, chosen based on their similarity to the current forecast and, in the current application, are represented by the one-hour average produced solar power.The AnEn performance for SPF is compared to a quantile regression (QR) technique and a persistence ensemble (PeEn) over three solar farms in Italy spanning different climatic conditions. The QR is a state-of-the-science method for probabilistic predictions that, similarly to AnEn, is based on a historical data set. The PeEn is a persistence model for probabilistic predictions, where the most recent 20 power measurements available at the same lead-time are used to form an ensemble. The performance assessment has been carried out evaluating important attributes of a probabilistic system such as statistical consistency, reliability, resolution and skill. The AnEn performs as well as QR for common events, by providing predictions with similar reliability, resolution and sharpness, while it exhibits more skill for rare events and during hours with a low solar elevation.

Temporal Filtering of InSAR Data Using Statistical Parameters From NWP Models

Finding solutions for the mitigation of atmospheric phase delay patterns from differential synthetic aperture radar interferometry (d-InSAR) observations is currently one of the most active research topics in radar remote sensing. Recently, many studies have analyzed the performance of regional numerical weather prediction (NWP) models for this task; however, despite the significant efforts made to optimize model parameterizations, most of these studies have concluded that current regional NWPs are not able to robustly reproduce the atmospheric phase delay structures that affect SAR interferograms. Despite these previous findings, we have revisited the application of NWPs for atmospheric correction using a different analysis strategy. In contrast to earlier studies, which assessed the quality of NWP-derived phase screen data, we have studied NWPs from a statistical angle by analyzing whether they are able to provide realistic information about the statistical properties of atmospheric phase signals in d-InSAR data. We have determined that NWP forecasts can provide relevant statistical information about the atmospheric phase screen captured in d-InSAR data. Based on this, this study presents a new atmospheric phase filtering approach that is using statistical atmospheric information as a prior in order to optimize the choice of unknown filter parameters. The mathematical concept of the prior-driven filtering approach is outlined, and its implementation is explained. We have determined the performance of this new filter concept and have shown that it comes very close to a filter optimum.

The Megha-Tropiques mission: a review after three years in orbit

The Megha-Tropiques mission is operating a suite of payloads dedicated to the documentation of the water and energy cycles in the intertropical region in a low inclination orbit. The satellite was launched in October, 2011 and we here review the scientific activity after the first three years of the mission. The microwave sounder (SAPHIR) and the broad band radiometer (SCARAB) are functioning nominally and exhibit instrumental performances well within the original specifications. The microwave imager, MADRAS, stopped acquisition of scientific data on January 26th, 2013 due to a mechanical failure. During its 16 months of operation, this radiometer experienced electrical issues making its usage difficult and delayed its validation. A suite of geophysical products has been retrieved from the Megha-Tropiques payloads, ranging from TOA radiative flux to water vapor profiles and instantaneous rain rates. Some of these geophysical products have been merged with geostationary data to provide, for instance, daily accumulation of rainfall all over the intertropical region. These products compare favorably with references from ground based or space-borne observation systems. The contribution of the mission unique orbit to its scientific objectives is investigated. Preliminary studies indicate a positive impact on both, humidity Numerical Weather Prediction forecasts thanks to the assimilation of SAPHIR Level 1 data, and on the rainfall estimation derived from the Global Precipitation Mission constellation. After a long commissioning phase, most of the data and the geophysical products suite are validated and readily available for further scientific investigation by the international community.

Hybrid machine learning forecasting of solar radiation values

The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. In this work we apply Support Vector Regression (SVR), Gradient Boosted Regression (GBR), Random Forest Regression (RFR) as well as a hybrid method to combine them to downscale and improve 3-h accumulated radiation forecasts provided by Numerical Weather Prediction (NWP) systems for seven locations in Spain. We use either direct 3-h aggregated radiation forecasts or we build first global accumulated daily predictions and disaggregate them into 3-h values, with both approaches outperforming the base NWP forecasts. We also show how to disaggregate the 3-h forecasts into hourly values using interpolation based on clear sky (CS) theoretical and experimental radiation models, with the disaggregated forecasts again being better than the base NWP ones and where empirical CS interpolation yields the best results. Besides providing ample background on a problem that offers many opportunities to the Machine Learning (ML) community, our study shows that ML methods or, more generally, hybrid artificial intelligence systems are quite effective and, hence, relevant for solar radiation prediction.

An initial assessment of observations from the Suomi‐NPP satellite: data from the Cross‐track Infrared Sounder (CrIS)

AbstractThis paper gives an appraisal of the hyperspectral Cross‐track Infrared Sounder (CrIS) on board the Suomi‐NPP satellite, for use in numerical weather prediction (NWP) at the Met Office. The quality of CrIS data is assessed by comparison with short‐range NWP forecasts, and the impact on forecasts is evaluated using results from assimilation experiments. The impact of assimilating CrIS and the complementary microwave sounder ATMS (Advanced Technology Microwave Sounding Unit) together is also presented.

Increasing horizontal resolution in numerical weather prediction and climate simulations: illusion or panacea?

The steady path of doubling the global horizontal resolution approximately every 8 years in numerical weather prediction (NWP) at the European Centre for Medium Range Weather Forecasts may be substantially altered with emerging novel computing architectures. It coincides with the need to appropriately address and determine forecast uncertainty with increasing resolution, in particular, when convective-scale motions start to be resolved. Blunt increases in the model resolution will quickly become unaffordable and may not lead to improved NWP forecasts. Consequently, there is a need to accordingly adjust proven numerical techniques. An informed decision on the modelling strategy for harnessing exascale, massively parallel computing power thus also requires a deeper understanding of the sensitivity to uncertainty--for each part of the model--and ultimately a deeper understanding of multi-scale interactions in the atmosphere and their numerical realization in ultra-high-resolution NWP and climate simulations. This paper explores opportunities for substantial increases in the forecast efficiency by judicious adjustment of the formal accuracy or relative resolution in the spectral and physical space. One path is to reduce the formal accuracy by which the spectral transforms are computed. The other pathway explores the importance of the ratio used for the horizontal resolution in gridpoint space versus wavenumbers in spectral space. This is relevant for both high-resolution simulations as well as ensemble-based uncertainty estimation.

Forecasting daily reference evapotranspiration for Australia using numerical weather prediction outputs

Farmers and irrigation system operators make real-time irrigation decisions based on a range of factors including short-term weather forecasts of rainfall and temperature. The simplest and oldest statistical method for forecasting daily ET0 is to use the long-term monthly mean ET0 based on historical observations. Forecasts of reference crop evapotranspiration (ET0) can be calculated from Numerical Weather Prediction (NWP) outputs and ET0 has the advantage of being more directly relevant to crop water requirements than temperature. This paper evaluates forecasts of ET0 made using the Bureau of Meteorology's operational NWP forecasts derived from the Australian Community Climate and Earth System Simulator – Global (ACCESS-G). The forecast performance for ET0 was quantified using the root mean squared error (RMSE), coefficient of determination (R2), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). Daily ET0 forecasts for lead times up to 9 days were compared against ET0 calculated using hourly observations from the 40 automatic weather stations across Australia.It was found that using NWP forecast daily ET0 was better than using the long-term monthly mean ET0 for lead times up to 6 days, beyond which the long-term monthly mean was better. The average MSSS for ET0 forecasts across all stations varied between 66% and 12% for lead times of 1–6 days, respectively. Further, it was found that forecast performance for daily ET0 was highest in autumn for tropical climates and lowest in spring for temperate climates. Errors in incoming solar radiation were the most important source of ET0 forecast error, followed by air temperature, dew point temperature and wind speed, for all lead times. Also, it was found that the forecast performances for incoming solar radiation and mean wind speed were relatively poor compared with the air and dew point temperatures