Operational Numerical Weather Prediction System Research Articles

A database of in situ water temperatures for large inland lakes across the coterminous United States

Water temperature dynamics in large inland lakes are interrelated with internal lake physics, ecosystem function, and adjacent land surface meteorology and climatology. Models for simulating and forecasting lake temperatures often rely on remote sensing and in situ data for validation. In situ monitoring platforms have the benefit of providing relatively precise measurements at multiple lake depths, but are often sparser (temporally and spatially) than remote sensing data. Here, we address the challenge of synthesizing in situ lake temperature data by creating a standardized database of near-surface and subsurface measurements from 134 sites across 29 large North American lakes, with the primary goal of supporting an ongoing lake model validation study. We utilize data sources ranging from federal agency repositories to local monitoring group samples, with a collective historical record spanning January 1, 2000 through December 31, 2022. Our database has direct utility for validating simulations and forecasts from operational numerical weather prediction systems in large lakes whose extensive surface area may significantly influence nearby weather and climate patterns.

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

AbstractEnsemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.

КРАТКОСРОЧНОЕ ПРОГНОЗИРОВАНИЕ СТОКА РЕК РОССИИ С ИСПОЛЬЗОВАНИЕМ МОДЕЛИ HBV-96 И СИСТЕМЫ COSMO-RU

A method for short-range forecasting of average daily streamflow is proposed. The method uses the HBV-96 conceptual model of river runoff formation and the COSMO-Ru operational numerical weather prediction system. The method has been implemented for 546 river basins located throughout Russia. An archive of the required hydrometeorological information for the period from 2010 to 2019 has been formed. The method has been developed using the data for the first seven years and verified on independent material with the ones for the last three years. The verification results have shown that the method allows obtaining satisfactory forecasts for a quite large number of river basins. The results make it possible to use the proposed method within the framework of an automated system for preparing and issuing short-range forecasts for the Russian river streamflow.

Displacement Error Characteristics of 500-hPa Cutoff Lows in Operational GFS Forecasts

Abstract Cutoff lows are often associated with high-impact weather; therefore, it is critical that operational numerical weather prediction systems accurately represent the evolution of these features. However, medium-range forecasts of upper-level features using the Global Forecast System (GFS) are often subjectively characterized by excessive synoptic progressiveness, i.e., a tendency to advance troughs and cutoff lows too quickly downstream. To better understand synoptic progressiveness errors, this research quantifies seven years of 500-hPa cutoff low position errors over the globe, with the goal of objectively identifying regions where synoptic progressiveness errors are common and how frequently these errors occur. Specifically, 500-hPa features are identified and tracked in 0–240-h 0.25° GFS forecasts during April 2015–March 2022 using an objective cutoff low and trough identification scheme and compared to corresponding 500-hPa GFS analyses. In the Northern Hemisphere, cutoff lows are generally underrepresented in forecasts compared to verifying analyses, particularly over continental midlatitude regions. Features identified in short- to long-range forecasts are generally associated with eastward zonal position errors over the conterminous United States and northern Asia, particularly during the spring and autumn. Similarly, cutoff lows over the Southern Hemisphere midlatitudes are characterized by an eastward displacement bias during all seasons. Significance Statement Cutoff lows are often associated with high-impact weather, including excessive rainfall, winter storms, and severe weather. GFS forecasts of cutoff lows over the United States are often subjectively noted to advance cutoff lows too quickly downstream, and thus limit forecast skill in potentially impactful scenarios. Therefore, this study quantifies the position error characteristics of cutoff lows in recent GFS forecasts. Consistent with typically anecdotal impressions of cutoff low position errors, this analysis demonstrates that cutoff lows over North America and central Asia are generally associated with an eastward position bias in medium- to long-range GFS forecasts. These results suggest that additional research to identify both environmental conditions and potential model deficiencies that may exacerbate this eastward bias would be beneficial.

GEFSv12 High- and Low-Skill Day-10 Tornado Forecasts

Abstract On average, modern numerical weather prediction forecasts for daily tornado frequency exhibit no skill beyond day 10. However, in this extended-range lead window, there are particular model cycles that have exceptionally high forecast skill for tornadoes because of their ability to correctly simulate the future synoptic pattern. Here, model initial conditions that produced a more skillful forecast for tornadoes over the United States were exploited while also highlighting potential causes for low-skill cycles within the Global Ensemble Forecasting System, version 12 (GEFSv12). There were 88 high-skill and 91 low-skill forecasts in which the verifying day-10 synoptic pattern for tornado conditions revealed a western U.S. thermal trough and an eastern U.S. thermal ridge, a favorable configuration for tornadic storm occurrence. Initial conditions for high skill forecasts tended to exhibit warmer sea surface temperatures throughout the tropical Pacific Ocean and Gulf of Mexico, an active Madden–Julian oscillation, and significant modulation of Earth-relative atmospheric angular momentum. Low-skill forecasts were often initialized during La Niña and negative Pacific decadal oscillation conditions. Significant atmospheric blocking over eastern Russia—in which the GEFSv12 overforecast the duration and characteristics of the downstream flow—was a common physical process associated with low-skill forecasts. This work helps to increase our understanding of the common causes of high- or low-skill extended-range tornado forecasts and could serve as a helpful tool for operational forecasters. Significance Statement This research provides a framework for the anticipation of a more (or less) skillful 10-day tornado forecast in an operational numerical weather prediction system. High-skill forecasts were associated with substantial tropical convection and warm sea surface temperature throughout the Pacific Ocean and Gulf of Mexico, whereas the underlying cause of low-skill forecasts were typically associated with a blocking anticyclone over eastern Russia. These findings are important because they permit increased or decreased confidence in a long-range forecast of tornado occurrence based on a dynamical prediction system.

Network Structure of Atmospheric Perturbations

Abstract Accurate atmospheric-state analysis is essential for understanding and prediction of the atmosphere and is a difficult scientific problem due to the chaotic nature of the atmosphere; namely, small atmospheric perturbations (APs) grow rapidly in nonlinear processes. The key to atmospheric-state analysis is knowing the structure of the APs. We analyzed the AP structure in terms of network theory using a 192-member AP ensemble. The AP ensemble was generated by an ensemble of variational data assimilation (DA) with the perturbed observation method using the operational numerical weather prediction system at the Japan Meteorological Agency. The generated APs captured flow-dependent AP structures corresponding to atmospheric normal modes, and their use in DA improved accuracy of atmospheric-state predictions. These show the usefulness of the APs. The network property of the APs are as follows. The atmosphere has a small average network distance compared with the square root of the number of nodes, and a large clustering coefficient (about 0.6). These show that the APs have small-world network properties. The degree distribution of APs shows the heavy-tailed structure. These three properties of APs are common to various complex networks in other systems. Hubs in the AP network correspond to atmospheric disturbances. The network community detection using network modularity shows about 18 communities with 0.8 modularity. These basic network properties of APs represent efficient information exchange in the atmosphere, which provides a complementary atmospheric picture to its traditional physical picture based on fluid dynamics and thermodynamics, and would be basic information for atmospheric sciences and extension of application target of the network theory. Significance Statement The purpose of this paper is to analyze properties of atmospheric perturbations using the network theory and an accurate ensemble of the atmospheric perturbations. This is important for atmospheric-state estimation and forecasting because the atmosphere is chaotic and small perturbations grow rapidly. Our results show that the atmosphere has a small average network distance and a large clustering coefficient, which are the properties of a small-world network, and a heavy-tailed degree distribution. These three properties of atmospheric perturbations are common to various complex networks in other systems.

Assimilation of surface‐sensitive bands' clear‐sky radiance data using retrieved surface temperatures from geostationary satellites

AbstractClear‐sky radiances (CSRs) derived from observations made by imager sensors on board geostationary satellites are widely used in most operational numerical weather prediction systems. CSRs have data on tropospheric water vapour and temperatures, and the products at water vapour bands are generally assimilated into global data assimilation systems. In another band, known as the CO2 band (13.3–13.4 μm), CSRs are not used widely yet, despite having a wealth of information about temperatures in the mid‐ and low troposphere. This is mainly because of the high surface sensitivity of this band, which makes it difficult to accurately simulate brightness temperatures when there are non‐negligible errors in the surface parameters in the models. This article quantitatively investigated the surface sensitivities of CO2 and water vapour bands, which have the sensitivity under dry atmospheric conditions, and developed retrieval of land surface temperature (LST) from window band CSRs to obtain a more accurate simulated brightness temperature. Additionally, it was discovered that the retrieved LST outperformed that from the model in short‐range forecasts for the low‐water‐vapour band (7.3 μm) CSR data assimilation, and throughout the forecasting period, especially in the tropics, for the CO2 band CSR data assimilation. We also examined an unexpected improvement in the low troposphere in the model's LST trials, and we concluded that it was related to the relationship between the LST and atmospheric temperature biases.

A hybrid data assimilation system based on machine learning

In the earth sciences, numerical weather prediction (NWP) is the primary method of predicting future weather conditions, and its accuracy is affected by the initial conditions. Data assimilation (DA) can provide high-precision initial conditions for NWP. The hybrid 4DVar-EnKF is currently an advanced DA method used by many operational NWP centres. However, it has two major shortcomings: The complex development and maintenance of the tangent linear and adjoint models and the empirical combination of the results of 4DVar and EnKF. In this paper, a new hybrid DA method based on machine learning (HDA-ML) is presented to overcome these drawbacks. In the new method, the tangent linear and adjoint models in the 4DVar part of the hybrid algorithm can be easily obtained by using a bilinear neural network to replace the forecast model, and a CNN model is adopted to fuse the analysis of 4DVar and EnKF to adaptively obtain the optimal coefficient of combination rather than the empirical coefficient as in the traditional hybrid DA method. The hybrid DA methods are compared with the Lorenz-96 model using the true values as labels. The experimental results show that HDA-ML improves the assimilation performance and significantly reduces the time cost. Furthermore, using observations instead of the true values as labels in the training system is more realistic. The results show comparable assimilation performance to that in the experiments with the true values used as the labels. The experimental results show that the new method has great potential for application to operational NWP systems.

A 3-D Cloud Detection Method for FY-4A GIIRS and Its Application in Operational Numerical Weather Prediction System

A 3-D Cloud Detection Method for FY-4A GIIRS and Its Application in Operational Numerical Weather Prediction System

Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System

AbstractConvective precipitation systems in the summer often cause sudden heavy precipitation and largely affect various human activities, but the rapid evolution limits our predicting capability. Phased‐array weather radars (PAWRs) with a high spatiotemporal resolution are useful for observing such precipitation system. A recently developed numerical weather prediction (NWP) system assimilates PAWR observations with a 500‐m mesh NWP model. It initiates 30‐min extended forecasts every 30 s, much more frequently than the operational NWP and nowcasting systems. This study investigates the benefits of the 30‐s‐updating NWP system in a single but representative convective precipitation event in which a convective cloud developed within 10 min, and its evolution was not well predicted by operational precipitation nowcasting. The rapidly updating NWP system successfully predicts the evolution of the convective cloud. Assimilating the PAWR observations every 30 s continuously modifies the moisture and dynamical fields and improves the forecast accuracy consistently.

Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency Using Spaceborne Radar and Microwave Imager of the Global Precipitation Measurement as Reference

Abstract In this study, the single-moment 6-class bulk cloud microphysics scheme used in the operational numerical weather prediction system at the Japan Meteorological Agency was improved using the observations of the Global Precipitation Measurement (GPM) core satellite as reference values. The original cloud microphysics scheme has the following biases: underestimation of cloud ice compared to the brightness temperature of the GPM Microwave Imager (GMI) and underestimation of the lower-troposphere rain compared to the reflectivity of GPM Dual-frequency Precipitation Radar (DPR). Furthermore, validation of the dual-frequency rate of reflectivity revealed that the dominant particles in the solid phase of simulation were graupel and deviated from DPR observation. The causes of these issues were investigated using a single-column kinematic model. The underestimation of cloud ice was caused by a high ice-to-snow conversion rate, and the underestimation of precipitation in the lower layers was caused by an excessive number of small-diameter rain particles. The parameterization of microphysics scheme is improved to eliminate the biases in the single-column model. In the forecast obtained using the improved scheme, the underestimation of cloud ice and rain is reduced. Consequently, the prediction errors of hydrometeors were reduced against the GPM satellite observations, and the atmospheric profiles and precipitation forecasts were improved.

The heavy precipitation event of 14–15 October 2018 in the Aude catchment: a meteorological study based on operational numerical weather prediction systems and standard and personal observations

Abstract. The case of the heavy precipitation event on 14 and 15 October 2018 which has led to severe flash flooding in the Aude watershed in south-western France is studied from a meteorological point of view using deterministic and probabilistic numerical weather prediction systems, as well as a unique combination of observations from both standard and personal weather stations. This case features typical characteristics of Mediterranean heavy precipitation events such as its classic synoptic situation and its quasi-stationary convective precipitation that regenerates continuously, as well as some peculiarities such as the presence of a former hurricane and a pre-existing cold air mass close to the ground. Mediterranean Sea surface temperature and soil moisture anomalies are briefly reviewed, as they are known to play a role in this type of hydrometeorological events. A study of rainfall forecasts shows that the event had limited predictability, in particular given the small size of the watersheds involved. It is shown that the stationarity of precipitation, whose estimation benefits from data from personal stations, is linked to the presence near the ground of a trough and a strong potential virtual temperature gradient, the stationarity of both of which is highlighted by a combination of observations from standard and personal stations. The forecast that comes closest to the rainfall observations contains the warmest, wettest, and fastest low-level jet and also simulates near the ground a trough and a marked boundary between cold air in the west and warm air in the east, both of which are stationary.

Evaluation of Three Numerical Weather Prediction Models for the Weddell Sea Region for the Austral Winter 2013

AbstractIt is widely recognized that numerical weather prediction (NWP) results for the Antarctic are relatively poor compared to the mid‐latitudes. In this study, we evaluate output from three operational NWP systems: the ECMWF, Global Forecast System (GFS) and Antarctic Mesoscale Prediction System (AMPS), for the Austral winter (June‐August) of 2013 for the Weddell Sea region, paying special attention to regional patterns of error statistics. This is the first evaluation of NWP systems over the Southern Ocean that also addresses the accuracy of forecasted vertical profiles. In the evaluation, we use data from land‐ and ship‐based automatic weather stations (AWS) and radiosoundings. While the ECMWF and AMPS forecasts are on average biased cold and dry near the surface, the GFS forecasts are on average biased warm and moist. The near‐surface wind speed is on average overestimated by the AMPS forecasts, whereas it is slightly underestimated by the forecasts of the other two NWP systems. Among the variables investigated, all three NWP systems forecast the near‐surface specific humidity most accurately, followed by the temperature and then the wind speed. The forecast quality for the near‐surface and upper‐air wind speed degrades the most rapidly with increasing lead time, compared to the other variables. ECMWF is the overall best NWP system when compared against both the near‐surface and upper‐air observations, followed by AMPS and then GFS. The generally poorest model performance is found in locations with complex terrain along the coast of the Antarctic continent, and the best over the ocean.

Forecast Impact of FORMOSAT‐7/COSMIC‐2 GNSS Radio Occultation Measurements

AbstractThe FORMOSAT‐7/COSMIC‐2 GNSS‐RO mission was launched on June 25, 2019, and it has provided a large increase in the number of GNSS‐RO observations available for operational numerical weather prediction (NWP) in the latitude band between ±40°. A key aim of this mission has been to improve the GNSS‐RO measurement quality in the lower and middle troposphere. In this study, we summarize the impact of the FORMOSAT‐7/COSMIC‐2 measurements in two independent NWP systems, which are now assimilating these measurements operationally. These are the United States Navy Global Environmental Model (NAVGEM) and the European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Both systems employ a 4‐dimensional variational system (4D‐Var), and assimilate GNSS‐RO bending angles. The experiments cover the period January to March 2020. The impact of the FORMOSAT‐7/COSMIC‐2 measurements is assessed using improvements in short‐range forecast departures to other observations such as radiosonde and radiances, forecast error statistics against a verifying analysis, and adjoint based Forecast Sensitivity to Observation Impact (FSOI) estimates. The FORMOSAT‐7/COSMIC‐2 measurement has a clear impact on stratospheric temperatures and winds in the tropics. A novel finding is that the measurements also improve the tropical tropospheric humidity fit to radiosondes, and the fit to tropospheric radiances sensitive to humidity. To date, the impact of GNSS‐RO on humidity has been difficult to demonstrate in well constrained, operational NWP systems assimilating the full suite of observations. The results are achieved with a conservative assimilation approach which extended the quality control and observation error assignments used for the previous COSMIC receivers; further, possible improvements to the assimilation strategy are noted.

Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere

AbstractWe present a significantly improved data‐driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework include an off‐line volume‐conservative mapping to a cubed‐sphere grid, improvements to the CNN architecture and the minimization of the loss function over multiple steps in a prediction sequence. The cubed‐sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN. Our improved model produces weather forecasts that are indefinitely stable and produce realistic weather patterns at lead times of several weeks and longer. For short‐ to medium‐range forecasting, our model significantly outperforms persistence, climatology, and a coarse‐resolution dynamical numerical weather prediction (NWP) model. Unsurprisingly, our forecasts are worse than those from a high‐resolution state‐of‐the‐art operational NWP system. Our data‐driven model is able to learn to forecast complex surface temperature patterns from few input atmospheric state variables. On annual time scales, our model produces a realistic seasonal cycle driven solely by the prescribed variation in top‐of‐atmosphere solar forcing. Although it currently does not compete with operational weather forecasting models, our data‐driven CNN executes much faster than those models, suggesting that machine learning could prove to be a valuable tool for large‐ensemble forecasting.

Impact of Doppler Radar Wind Observations on Australian High-Resolution Numerical Weather Prediction

Abstract The impact of Doppler radar wind observations on forecasts from a developmental, high-resolution numerical weather prediction (NWP) system is assessed. The new 1.5-km limited-area model will be Australia’s first such operational NWP system to include data assimilation. During development, the assimilation of radar wind observations was trialed over a 2-month period to approve the initial inclusion of these observations. Three trials were run: the first with no radar data, the second with radial wind observations from precipitation echoes, and the third with radial winds from both precipitation and insect echoes. The forecasts were verified against surface observations from automatic weather stations, against rainfall accumulations using fractions skill scores, and against satellite cloud observations. These methods encompassed verification across a range of vertical levels. Additionally, a case study was examined more closely. Overall results showed little statistical difference in skill between the trials, and the net impact was neutral. While the new observations clearly affected the forecast, the objective and subjective analyses showed a neutral impact on the forecast overall. As a first step, this result is satisfactory for the operational implementation. In future, upgrades to the radar network will start to reduce the observation error, and further improvements to the data assimilation are planned, which may be expected to improve the impact.

Enhanced parallelization of the incremental 4D‐Var data assimilation algorithm using the Randomized Incremental Optimal Technique

AbstractIncremental 4D‐Var is a data assimilation algorithm used routinely at operational numerical weather prediction (NWP) centres worldwide. The algorithm solves a series of quadratic minimization problems (inner‐loops) obtained from linear approximations of the forward model around nonlinear trajectories (outer‐loops). Since most of the computational burden is associated with the inner‐loops, many studies have focused on developing computationally efficient algorithms to solve the least‐square quadratic minimization problem, in particular through time parallelization. This paper presents the first implementation and testing of a recently proposed method for parallelizing incremental 4D‐Var, the Randomized Incremental Optimal Technique (RIOT), which replaces the traditional sequential conjugate gradient (CG) iterations in the inner‐loop of the minimization with fully parallel randomized singular value decomposition (RSVD) of the preconditioned Hessian of the cost function. RIOT is tested using the standard Lorenz‐96 model (L‐96) as well as two realistic high‐dimensional atmospheric source inversion problems based on aircraft observations of black carbon concentrations. A new outer‐loop preconditioning technique tailored to RSVD was introduced to improve convergence stability and performance. Results obtained with the L‐96 system show that the performance improvement from RIOT compared to standard CG algorithms increases significantly with nonlinearities. Overall, in the realistic black carbon source inversion experiments, RIOT reduces the wall‐clock time of the 4D‐Var minimization by a factor of 2 to 3, at the cost of a factor of 4 to 10 increase in energy cost due to the large number of parallel cores used. Furthermore, RIOT enables reduction of the wall‐clock time computation of the analysis‐error covariance matrix by a factor of 40 compared to a standard iterative Lanczos approach. Finally, as evidenced in this study, implementation of RIOT in an operational NWP system will require a better understanding of its convergence properties as a function of the Hessian characteristics and, in particular, the degree of freedom for signal of the inverse problem.

Evaluation of a sub-kilometre NWP system in an Arctic fjord-valley system in winter

Terrain challenges the prediction of near-surface atmospheric conditions, even in kilometre-scale numerical weather prediction (NWP) models. In this study, the ALADIN-HIRLAM NWP system with 0.5 km horizontal grid spacing and an increased number of vertical levels is compared to the 2.5-km model system similar to the currently operational NWP system at the Norwegian Meteorological Institute. The impact of the increased resolution on the forecasts’ ability to represent boundary-layer processes is investigated for the period from 12 to 16 February 2018 in an Arctic fjord-valley system in the Svalbard archipelago. Model simulations are compared to a wide range of observations conducted during a field campaign. The model configuration with sub-kilometre grid spacing improves both the spatial structure and overall verification scores for the near-surface temperature and wind forecasts compared to the 2.5-km experiment. The sub-kilometre experiment successfully captures the wind channelling through the valley and the temperature field associated with it. In a situation of a cold-air pool development, the sub-kilometre experiment has a particularly high near-surface temperature bias at low elevations. The use of measurement campaign data, however, reveals some encouraging results, e.g. the sub-kilometre system has a more realistic vertical profile of temperature and wind speed, and the surface temperature sensitivity to the net surface energy is closer to the observations. This work demonstrates the potential of sub-kilometre NWP systems for forecasting weather in complex Arctic terrain, and also suggests that the increase in resolution needs to be accompanied with further development of other parts of the model system.

GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST

Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of −0.63 in 2018.

The impact of Argo observations in a global weakly coupled ocean–atmosphere data assimilation and short‐range prediction system

AbstractThe Argo array is a large component of the ocean observing system on which operational ocean forecasts rely. Global observations of the sub‐surface temperature and salinity from Argo enables the accurate initialisation of ocean forecasts, improving the position of currents and the overall energy available for air–sea interactions. Such constraints on the sub‐surface ocean are important for coupled initialisation for seasonal forecasting where the atmosphere is coupled to the upper ocean. As operational centres move toward using coupled atmosphere–ocean models for numerical weather prediction (NWP), it is useful to understand how ocean observations contribute to short‐range coupled forecasts. Using the Met Office Weakly Coupled Data Assimilation system, we have run Observing System Experiments (OSEs) to investigate the effect of withholding Argo observations on short‐range coupled ocean–atmosphere analyses and forecasts. Withholding Argo significantly increases the sub‐surface temperature and salinity RMS errors with consequent changes in the upper ocean heat content and positions of currents. Overall, there is little systematic impact of these ocean differences on global atmosphere analyses. However, in a case‐study over the period of hurricane Sandy (October 2012), we do see that Argo observations have some impact on atmospheric forecasts. The assimilation of Argo observations affects the position of the Gulf Stream leading to a small effect on the position and intensity of hurricane Sandy as it made landfall. As the resolution of the system is quite low compared to operational NWP systems, seeing even a small impact is encouraging. We might expect with future increased‐resolution coupled models that this influence of ocean observations on the atmosphere would only increase.