Gridded Model Output Research Articles

Tracking the Evolution of Snow Drought in the U.S. Pacific Northwest at Variable Scales

AbstractLong‐term and year‐to‐year changes in climate can cause anomalously warm and/or dry conditions, leading to periods with snowfall deficits known as snow droughts. How the occurrence of distinct types of snow drought has evolved at different spatial and temporal scales is not well understood. Here, we investigate how snow droughts and associated seasonal snow conditions vary subseasonally in the US Pacific Northwest. We specifically consider differences with spatial unit (point vs. river basin), data set and intra‐annual timing of snow drought. Data sets included in the research come from in situ observations, gridded model outputs and optical remote sensing. We found that overall, snow drought occurrences have increased 10%–15% over the last 30 years based on decadal counts, but the change was non‐uniform across snow drought types and winter periods. Our research showed a decrease in dry snow droughts but an increase in warm/dry snow droughts according to in situ observations and only a modest increase in the model record. We also found that dry snow droughts were more prevalent in the eastern portion of our study region while warm/dry snow droughts were generally more common in the western part of the study area. With optical remote sensing data, it was possible to identify warm/dry snow drought conditions in the mid to late winter (January–May), but not possible to distinguish other types of snow drought in the early winter. This research points to the importance of data set choice, spatial resolution and spatial/temporal aggregation when selecting parameters for snow drought research.

Comparison of Met Office regional model soil moisture with COSMOS‐UK field‐scale in situ observations

AbstractThe UK Met Office state‐of‐the‐art, deterministic, convection‐permitting, coupled land‐atmosphere, regional weather forecasting system, known as the UKV or UK Variable resolution model (Tang et al. Meteorological Applications, 2013; 20:417–426), has been operational since 2015. Science updates are regularly made to the UKV land surface data assimilation scheme when those updates improve predictions of screen temperature and humidity, since these quantities have a direct impact on atmospheric states and weather forecasts. Less attention has been paid to whether UKV soil moisture analyses are close to independent, in‐situ soil moisture observations, partly because it is difficult to make meaningful comparisons between 1.5 km2 gridded model outputs and traditional point sensor measurements. Soil moisture is recognized to be important when hydrological forecasts for runoff and rivers are required. This is because soil moisture controls the extent to which rainfall can infiltrate the soil, and the amount of surface runoff affects the timing of peak river flows (Ward & Robinson, Principles of Hydrology. McGraw‐Hill Publishing Company; 2000; Singh et al. Water Resources Research, 2021, 57, e2020WR028827). Gómez et al. (Remote Sensing, 2020; 12:3691) report benefits to river flow forecasts when using soil moisture data assimilation in the UKV system instead of a daily downscaled product from the Met Office global model. The Met Office measures soil temperature and soil moisture at Cardington (Osborne & Weedon, Journal of Hydrometeorology, 2021, 22:279–295); there is no other UK Met Office site at which soil moisture is measured. In this study, we use field‐scale (~200 m radius) soil moisture measurements from the UK Centre for Ecology and Hydrology's (UKCEH's) COSMOS‐UK network to provide independent verification and analysis of UKV soil moisture during summer 2018, an unusually dry period in the United Kingdom. We find that the match to COSMOS‐UK soil moisture observations is generally good, and that changes made to the land data assimilation approach during a recent operational upgrade had a generally beneficial impact on UKV soil moisture analyses under very dry conditions.

A precipitation downscaling method using a super-resolution deconvolution neural network with step orography

Abstract Coarse spatial resolution in gridded precipitation datasets, reanalysis, and climate model outputs restricts their ability to characterize the localized extreme rain events and limits the use of the coarse resolution information for local to regional scale climate management strategies. Deep learning models have recently been developed to rapidly downscale the coarse resolution precipitation to the high local scale resolution at a much lower cost than dynamic downscaling. However, these existing super-resolution deep learning modeling studies have not rigorously evaluated the model’s skill in producing fine-scale spatial variability, particularly over topographic features. These current deep-learning models also have difficulty predicting the complex spatial structure of extreme events. Here, we develop a model based on super-resolution deconvolution neural network (SRDN) to downscale the hourly precipitation and evaluate the predictions. We apply three versions of the SRDN model: (a) SRDN (no orography), (b) SRDN-O (orography only at final resolution enhancement), and (c) SRDN-SO (orography at each step of resolution enhancement). We assess the ability of SRDN-based models to reproduce the fine-scale spatial variability and compare it with the previously used deep learning model (DeepSD). All the models are trained and tested using the Conformal Cubic Atmospheric Model (CCAM) data to perform a 100 to 12.5 km of hourly precipitation downscaling over the Australian region. We found that SRDN-based models, including orography, deliver better fine-scale spatial structures of both climatology and extremes, and significantly improved the deep-learning downscaling. The SRDN-SO model performs well both qualitatively and quantitatively in reconstructing the fine-scale spatial variability of climatology and rainfall extremes over complex orographic regions.

Imprint of intrinsic ocean variability on decadal trends of regional sea level and ocean heat content using synthetic profiles

The global ocean is warming and has absorbed 90% of the Earth Energy Imbalance over 2010–2018 leading to global mean sea level rise. Both ocean heat content (OHC) and sea level trends show large regional deviations from their global means. Both quantities have been estimated from in-situ observations for years. However, in-situ profile coverage is spatially uneven, leading to uncertainties when assessing both OHC and sea level trends, especially at regional scale. Recently, a new possible driver of regional sea level and OHC trends has been highlighted using eddy-permitting ensemble ocean simulations over multiple decades: non-linear ocean processes produce chaotic fluctuations, which yield random contributions to regional decadal OHC and sea level trends. In-situ measurements capture a combination of the atmospherically-forced response and this intrinsic ocean variability. It is therefore important to understand the imprint of the chaotic ocean variability recorded by the in-situ measurement sampling in order to assess its impact and associated uncertainty on regional budgets. A possible approach to investigate this problem is to use a set of synthetic in-situ-like profiles extracted from an ensemble of forced ocean simulations started from different states and integrated with the same atmospheric forcing. Comparisons between the original ensemble outputs and the remapped, subsampled, in-situ-like profiles elucidate the contribution of chaotic ocean variability to OHC and regional sea level trends. Our results show that intrinsic variability may be large in eddy-active regions in the gridded model outputs, and remains substantial when using the in-situ sampling-based estimates. Using the latter, the same result is also found on large scales, for which atmospheric forcing has been identified as the main driver. Our results suggest accounting for this intrinsic ocean variability when assessing regional OHC and sea level trend budgets on decadal time scales.

Observing Profiles of Derived Kinematic Field Quantities Using a Network of Profiling Sites

Abstract Observations of thermodynamic and kinematic parameters associated with derivatives of the thermodynamics and wind fields, namely, advection, vorticity, divergence, and deformation, can be obtained by applying Green’s theorem to a network of observing sites. The five nodes that comprise the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) profiling network, spaced 50–80 km apart, are used to obtain measurements of these parameters over a finite region. To demonstrate the applicability of this technique at this location, it is first applied to gridded model output from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model, using profiles from the locations of ARM network sites, so that values calculated from this method can be directly compared to finite difference calculations. Good agreement is found between both approaches as well as between the model and values calculated from the observations. Uncertainties for the observations are obtained via a Monte Carlo process in which the profiles are randomly perturbed in accordance with their known error characteristics. The existing size of the ARM network is well suited to capturing these parameters, with strong correlations to model values and smaller uncertainties than a more closely spaced network, yet it is small enough that it avoids the tendency for advection to go to zero over a large area.

Do CMIP models capture long-term observed annual precipitation trends?

This study provides a long-term (1891–2014) global assessment of precipitation trends using data from two station-based gridded datasets and climate model outputs evolved through the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). Our analysis employs a variety of modeling groups that incorporate low- and high-top level members, with the aim of assessing the possible effects of including a well-resolved stratosphere on the model’s ability to reproduce long-term observed annual precipitation trends. Results demonstrate that only a few regions show statistically significant differences in precipitation trends between observations and models. Nevertheless, this pattern is mostly caused by the strong interannual variability of precipitation in most of the world regions. Thus, statistically significant model-observation differences on trends (1891–2014) are found at the zonal mean scale. The different model groups clearly fail to reproduce the spatial patterns of annual precipitation trends and the regions where stronger increases or decreases are recorded. This study also stresses that there are no significant differences between low- and high-top models in capturing observed precipitation trends, indicating that having a well-resolved stratosphere has a low impact on the accuracy of precipitation projections.

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

AbstractSub‐seasonal forecasts are becoming more widely used in the energy sector to inform high‐impact, weather‐dependent decisions. Using pattern‐based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern‐based methods perform compared with gridded model output has not been completed. We compare four methods to predict weekly‐mean anomalies of electricity demand and demand‐net‐wind across 28 European countries. At short lead times (days 0–10) grid‐point forecasts have higher skill than pattern‐based methods across multiple metrics. However, at extended lead times (day 12+) pattern‐based methods can show greater skill than grid‐point forecasts. All methods have relatively low skill at weekly‐mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern‐based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision‐making, as it combines the dynamical robustness in the large‐scale flow conditions from the pattern‐based methods with local information present in the grid‐point forecasts.

Understanding Differences in California Climate Projections Produced by Dynamical and Statistical Downscaling

AbstractWe compare historical and end‐of‐century temperature and precipitation patterns over California from one dynamically downscaled simulation using the Weather Research and Forecast (WRF) model and two simulations statistically downscaled using Localized Constructed Analogs (LOCA). We uniquely separate causes of differences between dynamically and statistically based future climate projections into differences in historical climate (gridded observations versus regional climate model output) and differences in how these downscaling techniques explicitly handle future climate changes (numerical modeling versus analogs). In these methods, solutions between different downscaling techniques differ more in the future compared to the historical period. Changes projected by LOCA are insensitive to the choice of driving data. Only through dynamical downscaling can we simulate physically consistent regional springtime warming patterns across the Sierra Nevada, while the statistical simulations inherit an unphysical signal from their parent Global Climate Model (GCM) or gridded data. The results of our study clarify why these different techniques produce different outcomes and may also provide guidance on which downscaled products to use for certain impact analyses in California and perhaps other Mediterranean regimes.

The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

WMO Typhoon Landfall Forecast Demonstration Project (WMO-TLFDP) Progress and Future Plans

ABSTRACT The “WMO Typhoon Landfall Forecast Demonstration Project (TLFDP)” was started in May 2010 in conjunction with the start of the Shanghai World Expo 2010. The project was successful in terms of assisting the local forecasters in providing an efficient tropical cyclone (TC) forecast service for World Expo 2010, demonstrating the performance of the most up-to-date techniques in TC forecasting, and enhancing the ability of forecasters to effectively use products based on advanced TC forecasting techniques. The first phase was completed in December 2012 and the second phase was completed in December 2015. The third phase (TLFDP-III) started in 2016. During its first (2010-2012) and second (2013-2015) phases, TLFDP collected real-time TC forecast products from 15 Typhoon Forecast Product Providers (TFPPs). The products include deterministic track and intensity forecasts, ensemble track and intensity forecasts, deterministic wind radii forecasts, wind probability forecasts, and gridded model outputs. The products were disseminated through the project’s website and the operational website of the Shanghai Typhoon Warning Center. TLFDP has made significant progress in TC forecast verification, including setting up the tools for both realtime and post-season TC forecast verification, developing and integrating several new verification techniques, carrying out a survey on the operational status of TC forecast verification in the western North Pacific region, and contributing to the WMO document “Verification of tropical cyclone forecasts”. The post-season forecast verification was reported to the Session of the UNESCAP/WMO Typhoon Committee since 2013. New consensus methods for TC track and intensity forecasts were also proposed either based on Ensemble Prediction Systems (EPSs) products or multiple deterministic NWP model products. Two training workshops on TC forecasting were organized by the TLFDP and held in Shanghai, China, in May 2010 and June 2012, respectively, and seven research fellowship projects were implemented as jointly supported by the UNESCAP/WMO Typhoon Committee, Shanghai Typhoon Institute and Hong Kong Observatory. In its third phase (TLFDP-III, 2016-2018), the project will continue its effort on demonstration and evaluation of the newly developed forecasting techniques for TCs, and developing and documenting advanced guidance for the verification of TC forecasts, with special attention to TC intensity and precipitation.

What are the Best Thermodynamic Quantity and Function to Define a Front in Gridded Model Output?

AbstractFronts can be computed from gridded datasets such as numerical model output and reanalyses, resulting in automated surface frontal charts and climatologies. Defining automated fronts requires quantities (e.g., potential temperature, equivalent potential temperature, wind shifts) and kinematic functions (e.g., gradient, thermal front parameter, and frontogenesis). Which are the most appropriate to use in different applications remains an open question. This question is investigated using two quantities (potential temperature and equivalent potential temperature) and three functions (magnitude of the horizontal gradient, thermal front parameter, and frontogenesis) from both the context of real-time surface analysis and climatologies from 38 years of reanalyses. The strengths of potential temperature to identify fronts are that it represents the thermal gradients and its direct association with the kinematics and dynamics of fronts. Although climatologies using potential temperature show features associated with extratropical cyclones in the storm tracks, climatologies using equivalent potential temperature include moisture gradients within air masses, most notably at low latitudes that are unrelated to the traditional definition of a front, but may be representative of a broader definition of an airmass boundary. These results help to explain previously published frontal climatologies featuring maxima of fronts in the subtropics and tropics. The best function depends upon the purpose of the analysis, but Petterssen frontogenesis is attractive, both for real-time analysis and long-term climatologies, in part because of its link to the kinematics and dynamics of fronts. Finally, this study challenges the conventional definition of a front as an airmass boundary and suggests that a new, dynamically based definition would be useful for some applications.

A statistical downscaling framework for environmental mapping

In recent years, knowledge extraction from data has become increasingly popular, with many numerical forecasting models, mainly falling into two major categories—chemical transport models (CTMs) and conventional statistical methods. However, due to data and model variability, data-driven knowledge extraction from high-dimensional, multifaceted data in such applications require generalisations of global to regional or local conditions. Typically, generalisation is achieved via mapping global conditions to local ecosystems and human habitats which amounts to tracking and monitoring environmental dynamics in various geographical areas and their regional and global implications on human livelihood. Statistical downscaling techniques have been widely used to extract high-resolution information from regional-scale variables produced by CTMs in climate model. Conventional applications of these methods are predominantly dimensional reduction in nature, designed to reduce spatial dimension of gridded model outputs without loss of essential spatial information. Their downside is twofold—complete dependence on unlabelled design matrix and reliance on underlying distributional assumptions. We propose a novel statistical downscaling framework for dealing with data and model variability. Its power derives from training and testing multiple models on multiple samples, narrowing down global environmental phenomena to regional discordance through dimensional reduction and visualisation. Hourly ground-level ozone observations were obtained from various environmental stations maintained by the US Environmental Protection Agency, covering the summer period (June–August 2005). Regional patterns of ozone are related to local observations via repeated runs and performance assessment of multiple versions of empirical orthogonal functions or principal components and principal fitted components via an algorithm with fully adaptable parameters. We demonstrate how the algorithm can be extended to weather-dependent and other applications with inherent data randomness and model variability via its built-in interdisciplinary computational power that connects data sources with end-users.

Statistical Inversion of Surface Ocean Kinematics from Sea Surface Temperature Observations

AbstractThe sea surface temperature (SST) record provides a unique view of the surface ocean at high spatiotemporal resolution and holds useful information on the kinematics underlying the SST variability. To access this information, we develop a new local matrix inversion method that allows us to quantify the evolution of a given SST perturbation with a response function and to estimate velocity, diffusivity, and decay fields associated with it. The matrix inversion makes use of the stochastic climate model paradigm—we assume that SST variations are governed by a linear transport operator and a forcing that has a relatively short autocorrelation time scale compared to that of SST. We show that under these assumptions, the transport operator can be inverted from the covariance matrices of the underlying SST data. The accuracy of the results depends on the length of the time series, and in general the inverted properties depend on the spatial and time resolution of the SST data. Future studies could use the methodology to explore the interannual variability of SST anomalies; to estimate the scale dependency of ocean mixing; and to estimate anomaly propagation, both at the surface and in the interior. The methodology can be easily used with any gridded observations or model output with adequate time and spatial resolution, and it is not restricted to SST. The inversion code is written in Python and distributed as a MicroInverse package through GitHub and the Python Package Index.

An optimized data fusion method and its application to improve lateral boundary conditions in winter for Pearl River Delta regional PM2.5 modeling, China

Lateral boundary conditions (LBCs) are essential for chemical transport models to simulate regional transport; however they often contain large uncertainties. This study proposes an optimized data fusion approach to reduce the bias of LBCs by fusing gridded model outputs, from which the daughter domain's LBCs are derived, with ground-level measurements. The optimized data fusion approach follows the framework of a previous interpolation-based fusion method but improves it by using a bias kriging method to correct the spatial bias in gridded model outputs. Cross-validation shows that the optimized approach better estimates fused fields in areas with a large number of observations compared to the previous interpolation-based method. The optimized approach was applied to correct LBCs of PM2.5 concentrations for simulations in the Pearl River Delta (PRD) region as a case study. Evaluations show that the LBCs corrected by data fusion improve in-domain PM2.5 simulations in terms of the magnitude and temporal variance. Correlation increases by 0.13–0.18 and fractional bias (FB) decreases by approximately 3%–15%. This study demonstrates the feasibility of applying data fusion to improve regional air quality modeling.

Caliver: An R package for CALIbration and VERification of forest fire gridded model outputs.

The name caliver stands for CALIbration and VERification of forest fire gridded model outputs. This is a package developed for the R programming language and available under an APACHE-2 license from a public repository. In this paper we describe the functionalities of the package and give examples using publicly available datasets. Fire danger model outputs are taken from the modeling components of the European Forest Fire Information System (EFFIS) and observed burned areas from the Global Fire Emission Database (GFED). Complete documentation, including a vignette, is also available within the package.

Hydrological modelling using proxies for gauged precipitation and temperature

AbstractPrecipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high‐resolution models.

Rapid aggregation of global gridded crop model outputs to facilitate cross-disciplinary analysis of climate change impacts in agriculture

We discuss an on-line tool that facilitates access to the large collection of climate impacts on crop yields produced by the Agricultural Model Intercomparison and Improvement Project. This collection comprises the output of seven crop models which were run on a global grid using climate data from five different general circulation models under the current set of representative pathways. The output of this modeling endeavor consists of more than 36,000 publicly available global grids at a spatial resolution of one half degree. We offer flexible ways to aggregate these data while reducing the technical barriers implied by learning new download platforms and specialized formats. The tool is accessed trough any standard web browser without any special bandwidth requirement.

Recent changes in north-west Greenland climate documented by NEEM shallow ice core data and simulations, and implications for past-temperature reconstructions

Abstract. Combined records of snow accumulation rate, δ18O and deuterium excess were produced from several shallow ice cores and snow pits at NEEM (North Greenland Eemian Ice Drilling), covering the period from 1724 to 2007. They are used to investigate recent climate variability and characterise the isotope–temperature relationship. We find that NEEM records are only weakly affected by inter-annual changes in the North Atlantic Oscillation. Decadal δ18O and accumulation variability is related to North Atlantic sea surface temperature and is enhanced at the beginning of the 19th century. No long-term trend is observed in the accumulation record. By contrast, NEEM δ18O shows multidecadal increasing trends in the late 19th century and since the 1980s. The strongest annual positive δ18O values are recorded at NEEM in 1928 and 2010, while maximum accumulation occurs in 1933. The last decade is the most enriched in δ18O (warmest), while the 11-year periods with the strongest depletion (coldest) are depicted at NEEM in 1815–1825 and 1836–1846, which are also the driest 11-year periods. The NEEM accumulation and δ18O records are strongly correlated with outputs from atmospheric models, nudged to atmospheric reanalyses. Best performance is observed for ERA reanalyses. Gridded temperature reconstructions, instrumental data and model outputs at NEEM are used to estimate the multidecadal accumulation–temperature and δ18O–temperature relationships for the strong warming period in 1979–2007. The accumulation sensitivity to temperature is estimated at 11 ± 2 % °C−1 and the δ18O–temperature slope at 1.1 ± 0.2 ‰ °C−1, about twice as large as previously used to estimate last interglacial temperature change from the bottom part of the NEEM deep ice core.

Two sample inference for the second-order property of temporally dependent functional data

Motivated by the need to statistically quantify the difference between two spatio-temporal datasets that arise in climate downscaling studies, we propose new tests to detect the differences of the covariance operators and their associated characteristics of two functional time series. Our two sample tests are constructed on the basis of functional principal component analysis and self-normalization, the latter of which is a new studentization technique recently developed for the inference of a univariate time series. Compared to the existing tests, our SN-based tests allow for weak dependence within each sample and it is robust to the dependence between the two samples in the case of equal sample sizes. Asymptotic properties of the SN-based test statistics are derived under both the null and local alternatives. Through extensive simulations, our SN-based tests are shown to outperform existing alternatives in size and their powers are found to be respectable. The tests are then applied to the gridded climate model outputs and interpolated observations to detect the difference in their spatial dynamics.

Advances in a Distributed Approach for Ocean Model Data Interoperability

An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.