Forecasters Research Articles

Quantifying the value of probabilistic forecasts when trading renewable hybrid power parks in day-ahead markets: A Nordic case study

Renewable hybrid power parks (HPPs) that combine wind power, solar photovoltaic (PV) power and storage have emerged as promising electricity generation resources. However, HPPs face operational challenges due to the uncertainty in power production and electricity prices, which is why probabilistic forecasts that capture the uncertainty associated with forecast errors have gained attention. While the research community has proposed several methods to improve the accuracy of probabilistic forecasts, the question on how these forecasts can improve decision-making over deterministic forecasts is rarely quantified. This study assesses the value of probabilistic forecasts and analyze the improvement compared to deterministic forecasts in day-ahead markets. The value is quantified using almost two years of data from an operational HPP in Sweden. Results show that: (i) high grid connection capacities leverage the value of probabilistic models, (ii) a deterministic model is preferable for parks with a ratio of battery energy capacity to installed nominal power of the renewable power park equal to 0.6 MWh/MW, (iii) a probabilistic model allows utilizing the energy storage more effectively by reducing the energy throughput of the battery with 61%–87%, and (iv) a probabilistic model increases the unit profit when the forecast errors of the regulating price are higher than the spot price, (v) a simple probabilistic benchmark model, which is worse in terms of forecast accuracy, increases the unit profit compared to the analyzed deterministic models, and (vi) the more advanced probabilistic model analyzed in this study does not provide a significant improvement over a simple probabilistic benchmark model.

A hybrid deep learning-based short-term forecast model for ionospheric foF2 in East Asia region

The short-term forecast of the critical frequency of the ionospheric F2 layer (foF2) is particularly challenging due to its nonlinear and non-stationary characteristics. To improve the short-term forecast accuracy of foF2, we propose a short-term hybrid deep learning model that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and long short-term memory (LSTM) algorithms. The proposed model leverages the ICEEMDAN decomposition algorithm to expand foF2 time series data into multi-dimensional space and utilizes the LSTM algorithm to preserve long-term data information, capturing detailed changes in foF2 parameters and obtaining internal information about foF2. The 2014 and 2017 foF2 data from four observation stations located in Mohe, Beijing, Jeju, and Kokubunji in the mid-latitudes of East Asia were utilized for modeling and 1-hour forecast analysis. The forecast values of the proposed model align closely with the measured data, exhibiting minimal fluctuation in forecast error and effectively tracking the changing trend of foF2. The average root mean square error (RMSE) across the four stations in 2014 is 0.40 MHz, with a relative RMSE (RRMSE) of 7.11 % and a coefficient of determination (R2) of 0.98. The average RMSE in 2017 is 0.43 MHz, RRMSE is 7.68 %, and R2 is 0.92. The proposed model reveals significant improvements in the forecast accuracy of the comparison model compared with the CCIR, URSI, NN, and LSTM models. Especially compared with the CCIR and URSI models in the International Reference Ionosphere (IRI), demonstrating superior real-time tracking capability for foF2 and effectively addressing the challenge of poor short-term forecast accuracy observed in the IRI model.

Regime-Dependent Characteristics and Predictability of Cold-Season Precipitation Events in the St. Lawrence River Valley

Abstract The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C. Significance Statement Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.

Enhancing Surface Wind Speed and Temperature Prediction Using Surface-Layer Emulator and Transfer Learning

Abstract Accurate prediction of surface wind speed and temperature is crucial for many sectors. Physical schemes in numerical weather prediction (NWP) and data-driven correction approaches have limitations due to uncertainties in parameterization and lack of robustness, respectively. This study introduces a physics-informed data model, PhyCorNet, which combines a deep learning–based physics emulator (PhyNet) and a subsequent correction network (CorNet). PhyNet imitates the revised surface-layer parameterization scheme [default option of the Weather Research and Forecasting (WRF) Model]. CorNet refines predictions by mitigating the difference between PhyNet and observations. PhyCorNet enables gridded forecasts despite the training data being point based. Therefore, PhyCorNet can be regarded as a rediagnostic scheme for surface wind speed at 10 m (WS10) and temperature at 2 m (T2). Compared to WRF, it reduced diagnostic root-mean-square errors in the 24-h forecasts for WS10 and T2 by 40% and 36%, respectively, across China, with unbiased forecasts at almost all sites. PhyCorNet addresses the oversmoothing prediction issue of other deep learning models by providing the ability to represent fine-scale features and perform well in statistically extreme samples. In grid cells without observations for training, PhyCorNet performed much better than WRF, demonstrating the zero-shot learning capability. This study implies that the emulator plus bias correction provided by PhyCorNet could be used as a simple but effective approach to improve the performance of other diagnostic quantities in NWP. Significance Statement We developed a new model called PhyCorNet to improve the surface wind speed and temperature forecasts. Existing weather forecast models have limitations in accurately predicting these variables. PhyCorNet first uses a neural network (PhyNet) to mimic an existing physics-based wind and temperature calculation method. Another neural network [correction network (CorNet)] improves the PhyNet forecasts by comparing them with observations. Across China, PhyCorNet reduced 24-h forecast errors for wind speed by 40% and temperature by 36% compared to a conventional model. It performed well under diverse conditions and overcame the oversmoothing issues faced by other machine learning models. Importantly, PhyCorNet outperformed traditional models in areas without historical observations. We suggest that this new framework can also be used to enhance the forecasts of other atmospheric variables.

The Role of Antecedent Winter Soil Moisture Carryover on Spring Runoff Predictability in Snow-Influenced Western U.S. Catchments

Abstract Spring runoff is critical to agricultural, industrial, and municipal water supply as well as environmental uses in the western United States. Although spring runoff in this region has long been predicted based on late winter and early spring montane snow water storage, the role of soil moisture carryover from the previous winter is less understood and utilized in forecasts. We quantify the relationship between antecedent winter soil moisture and spring runoff for 85 unmanaged catchments in the western United States. Using a regression-based approach, we estimate the proportional reduction in error in seasonal runoff forecasts associated with soil moisture. We classify the catchments into two regimes: interior (cold and relatively dry) and maritime (warmer and wetter), based on seasonal variations in soil moisture. Soil moisture generally is relatively unchanged through the winter period in interior catchments, whereas it increases (mostly gradually, but occasionally rapidly) in maritime catchments. We find that winter temperature dominates soil moisture variability in both types of catchments. We find two patterns in the predictability of spring runoff. First, including antecedent winter soil moisture as a predictor enhances forecast accuracy and variance explanation in spring runoff for both interior and maritime catchments, particularly and with greater significance in interior catchments. Second, spring runoff prediction skill from antecedent winter soil moisture increases with elevation. Overall, we find strong evidence that the use of antecedent soil moisture can improve spring runoff forecast skill for catchments in the western United States. Significance Statement Spring runoff forecasts are crucial for water supply in the western United States, but typically exclude information about soil moisture despite its key role in the water balance. Our study explores how and why incorporating information about winter’s soil moisture levels affects spring runoff forecasts across 85 catchments in the western United States. We find that doing so can produce modest improvements in the prediction skill of spring runoff for both interior and maritime catchments but is particularly noticeable in interior catchments and at higher elevations where temperatures are lower. Overall, our research strongly supports the idea that using information about soil moisture from the previous winter can improve accuracy in spring runoff forecasts for the western United States.

From Hindsight to Foresight: Understanding and Overcoming Bias in Financial Forecasts

Abstract This study conducts a comprehensive benchmarking analysis on analyst forecasts of revenue and EBITDA for exchange-traded public companies in the U.S. spanning the period from 2009 to 2022. Our findings reveal a consistent and statistically significant upward bias present in both revenue and EBITDA forecasts, on average, as evidenced by various measures of forecast error. This bias is explored at both market sector and market-wide levels of aggregation to check for macro-consistency. Recognizing the significance of these biases, we propose bias-adjustment frameworks aimed at enhancing the accuracy of future analyst forecasts pertaining to revenue and EBITDA. The implications of our study underscore the importance of mitigating such biases for improved forecasting precision in financial markets.

Oil market responses to Sino–European political relation shock: Insights after China's world trade organization accession

We investigate how changes in political relations between Europe and China affect the oil market. When political relations improve, we find a significant increase in oil demand and prices, accompanied by a decrease in market uncertainty. However, the effects on oil supply and inventory are minimal. Additionally, we compute the forecast error variance decomposition and construct the counterfactual evolution of oil variables in the absence of the impacts of political relation shocks. We find that shifts in Sino–European political relations contribute more significantly to variations in oil market uncertainty than to other oil variables. Finally, we augment the vector autoregression model by incorporating the Sino–US political relationship index. Our findings reveal that this augmentation significantly changes the impacts.

The Drivers of Sustainable Development in South African Economy: The Role of Female Advancement

Contemporary societies have challenges in achieving sustainable development, while African economies remain quite distant from this ideal course for sustainable growth. Likewise, official statistics in South Africa show a wide gap between male and female involvement in economic activities. Given this, the study investigates the connection between three dimensions of sustainable development and female advancement in South Africa using auto-regressive disturbed lags (ARDL), Structural Vector autoregressive (SVAR) and Ganger non-causality approach. The three dimensions of sustainable development considered in this study are economic, environmental and social, proxies by adjusted net national savings, carbon emissions and adjusted net national income per capita, respectively. The female advancement indicators are proxies by employment, wages and salaries, and secondary school enrolment. The result from the ARDL shows that female advancement indicators promote short (SR) and long-run (LR) economic sustainability. Also, female wages and salaries have a positive relationship with carbon emissions in the SR but not significantly in the LR. At the same time, female employment and secondary school enrolment have a positive relationship with carbon emissions in the SR but are negative in the LR. The primary causes affecting long-term environmental sustainability are GDP per capita, financial development, population density, trade openness and urbanisation. Also, female advancement indicators promote social sustainability, especially employment, wages and salaries. More so, the SVAR results show that the forecasting error shock of GDP has minimal effect on carbon emission with notable variation in the population density. The causality result shows a unidirectional relationship between economic sustainability and female secondary school enrolment, environmental sustainability and female secondary school enrolment, and social sustainability and female secondary school enrolment, all flowing to female secondary school enrolment. Based on the findings, the study recommended improving female educational standards and increasing involvement in economic participation.

On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport

Abstract This study examines the application of machine learning to enhance wind nowcasting by using a Kolmogorov-Arnold Network model to improve predictions from the Global Forecast System at Madeira International Airport, a site affected by complex terrain. The research addresses the limitations of traditional numerical weather prediction models, which often fail to accurately forecast localized wind patterns. Using the Kolmogorov-Arnold Network model led to a substantial reduction in wind speed and direction forecast errors, with a performance that reached a 48.5% improvement to the Global Forecast System 3 h nowcast, considering the mean squared error. A key outcome of this study comes from the model’s ability to generate mathematical formulas that provide insights into the physical and mathematical dynamics influencing local wind patterns and improve the transparency, explainability, and interpretability of the employed machine learning models for atmosphere modeling.

Long term demand forecasting in Jakarta distribution grid system using fuzzy logic and artificial neural network method

Abstract In order to attain equilibrium between energy supply and demand, reliance on conventional methods for precise long-term electricity demand forecasting is no longer viable. The utilization of artificial intelligence, such as fuzzy logic and artificial neural network (ANN) models, emerges as a prospective solution in the current dynamic scenario. This research explores long-term electricity demand forecasting within the Jakarta distribution grid system, employing various fuzzy logic and ANN approaches including Sugeno, Mamdani, Bayesian Regularization, and the Levenberg algorithm. The analysis incorporates time series data spanning 2016 to 2019, encompassing electricity load demand, economic factors, and demographic variables, processed using MATLAB. The outcomes of the four forecasting methods reveal an average error range of 2 to 3%. The findings indicate that employing fuzzy logic and ANN methods for long-term electricity demand forecasting can yield a forecast error of less than 3%. The study recommends future research enhancements through the inclusion of additional time series data and a more refined system.

Comparative analysis of the predictive power of statistical models of macroeconomic indicators in conditions of permanent crises

Subject. The article discusses prediction of the state of the economy, the accuracy of forecasts of traditional models during crises, the need to find more effective model specifications to predict macro indicators. Objectives. The purpose is to carry out a comparative analysis of the predictive ability of ensemble methods in comparison with a set of models, including traditional statistical algorithms and machine learning algorithms. Methods. The comparative analysis of predictive ability of the models and interpretation of results obtained were performed using a dynamic factor model (DFM), a neural network with long-term short-term memory (LSTM), and integrated gradient methods (IG). Results. We performed the analysis of predictive ability of the ensemble model to forecast GDP, which combines DFM and LSTM to account for both linear and nonlinear dependencies in the data; the analysis of predictive power of various indicators, which showed that an increase in forecast error is observed for all models except DFM, the ensemble model with an error correction structure, and ARMAX. The obtained results can be used to build models of macroeconomic indicators in order to make strategic decisions by enterprises of various industries operating in a highly uncertain environment. Conclusions. The combination of DFM and LSTM in the ensemble provides higher accuracy forecasts than LSTM and competitor models, however, with less predictive power than DFM.

PREDIKSI PENJUALAN MELALUI METODE WEIGHTED MOVING AVERAGE (WMA) PADA UMKM “Nasi Bakar”

This study aims to find out the results of sales forecasting calculations using the Weight Moving Average method in Nurochmah Nasi Bakar MSMEs from April 2023 to July 2024. In addition, to determine the accuracy of forecast errors in sales forecasting calculations and the application of the Weighted Moving Average (WMA) method in predicting sales to Nurochmah MSMEs. The research method used is descriptive quantitative using historical sales data from January 2023 to July 2024, to predict sales using the WMA method at Nasi Bakar Nurochmah MSMEs from April 2023 to July 2024 using Production and Operations Management-Quality Control (POM-QM) software. The results of the calculation of sales forecasting using the WMA method on Nurochmah grilled rice MSMEs for April 2023 to July 2024 fluctuated as follows: December was 768.4 packs, a decrease of 4.547% from the actual 805 packs. January was 772.7 packs, a decrease of 0.553% from the actual 777 packs, February was 781.4 packs, an increase of 5.31% from the actual 742 packs. The accuracy of forecast error in calculating sales forecasting using the WMA method from April 2023 to July 2024 obtained an accuracy value of < 10% measured using the Mean Absolute Percent Error (MAPE). The average MAPE is 1.983%, it can be said that the forecasting ability of this method is very accurate. This shows that the WMA method can be applied to Nurochmah grilled rice MSMEs, so that it can help control sales volume, as well as as a basis for decision-making and planning sales targets for the next period.

Kausalitas Antara Variabel Makro dan Stunting di Sumatera Barat Periode 2021 -2023

This research intends to look at the causal relationship between stunting, unemployment, poverty, income inequality, human development index in West Sumatra for the period 2021 - 2023. The type of data used is secondary data in the form of panel data from the Central Statistics Agency (BPS) and the Indonesian Nutrition Status Survey from 2021 to 2023 mapping of 19 regencies/cities in West Sumatra. The data analysis used is vector auto regression analysis (VAR) and vector error correction model (VECM). In inductive analysis there are several tests, namely Optimum Lag Test, Model Stability Test, Johansen Cointegration Test, Granger Causality Test (Granger Causality), VECM Estimation, Impulse Response Function (IRF) Analysis, Forecast Error Variance Decomposition (FEVD) The results of this study show that there is a significant relationship between the human development index and income inequality. There is a significant relationship between unemployment and poverty and income inequality significant relationship between stunting and HDI, poverty, income inequality and unemployment.

The Forecasting of the Spread of Infectious Diseases Based on Conditional Generative Adversarial Networks

New epidemics encourage the development of new mathematical models of the spread and forecasting of infectious diseases. Statistical epidemiology data are characterized by incomplete and inexact time series, which leads to an unstable and non-unique forecasting of infectious diseases. In this paper, a model of a conditional generative adversarial neural network (CGAN) for modeling and forecasting COVID-19 in St. Petersburg is constructed. It takes 20 processed historical statistics as a condition and is based on the solution of the minimax problem. The CGAN builds a short-term forecast of the number of newly diagnosed COVID-19 cases in the region for 5 days ahead. The CGAN approach allows modeling the distribution of statistical data, which allows obtaining the required amount of training data from the resulting distribution. When comparing the forecasting results with the classical differential SEIR-HCD model and a recurrent neural network with the same input parameters, it was shown that the forecast errors of all three models are in the same range. It is shown that the prediction error of the bagging model based on three models is lower than the results of each model separately.

Determinants of the Developed Country Index and Indonesian Macroeconomic on the IDX Growth 30 and IDX Value 30

This study analyzes the determinants of the Developed Country Index (Hang Seng Index, Dow Jones Index) and Indonesian macroeconomic variables (10-Year Government Bond Yield, Foreign Ownership in Government Bonds, and the BI-7 Day Reverse Repo Rate, BI7DRR) on the movement of the IDX Growth 30 Index (IDXG30) and IDX Value 30 Index (IDXV30) from 2018–2022. Monthly time series data is used with a saturated sampling technique, processed via Eviews 12 using VECM analysis. The study conducts Stationarity, Optimal Lag, VAR Stability, and Cointegration Tests, along with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses Results show that the Hang Seng Index and Foreign Ownership in Government Bonds negatively affect IDXG30 and IDXV30, while BI7DRR positively influences both. The Dow Jones Index positively affects IDXG30 but negatively impacts IDXV30. Meanwhile, the 10-Year Government Bond Yield negatively affects IDXG30 and positively influences IDXV30. Additionally, the performance of IDXG30 and IDXV30 themselves and BI7DRR significantly contribute to both indices' movements.

Global atmospheric state analysis using objective Gaussian probability density functions

Atmospheric state analysis is a difficult scientific problem due to the chaotic nature of the atmosphere. Data assimilation is a framework for generating an accurate state analysis of a physical system using probability density functions (PDFs) describing uncertainty of information on the state of the physical system. However, since PDFs cannot be deduced theoretically, those used in data assimilation of atmospheric state analysis are based on empirical tunings. This PDF uncertainty limits the theoretical consistency and accuracy of atmospheric state analysis and that of all atmospheric sciences. In this study, we constructed a highly accurate and theoretically consistent atmospheric state analysis by objectively estimating the PDFs of all datasets (forecasts and observations) under the Gaussian approximation. We show that an ensemble of data assimilations with 192 members using the four-dimensional variational method and sample statistics obtained with the data assimilation theory (Desroziers’ method) can generate more accurate objective Gaussian PDFs, including flow-dependent forecast error structures. Numerical experiments of atmospheric state analysis and forecasts using objective PDFs were conducted and compared with those using conventional empirical PDFs. The objective PDFs had smaller error variances for most data (about 34% of those of CNTL on average) and larger observation error correlations for satellite radiances, where the strongest correlation was greater than 0.8. The analysed atmospheric states are systematically different, such as a cooler (exceeding 1.2 K) and wetter (exceeding 1.2 g/kg) low troposphere in regions characterized by low-level clouds off the west coast of the continents. The theoretical consistency evaluated by the chi-square-based tests showed a clear improvement from 16 to 95%. The forecast accuracy was improved globally up to 9%, with 95% statistical significance. The tropical cyclone track forecast accuracy was also improved about 20%.

Predictive ARIMA Model with a Machine Learning (ML) Approach for COVID-19 Data in Pakistan

This study is based on the application of an ARIMA (p, d, q) based machine learning (ML) approach to evaluate the dynamics of COVID-19 pandemic. The focus is on estimating epidemic trends and performing diagnostic scrutiny with model fitting. The data including all four waves of the pandemic pertaining to Pakistan, covering all four provinces (Sindh, Punjab, Khyber Pakhtunkhwa, Balochistan, as well as Gilgit Baltistan, Azad Jammu Kashmir, and the capital city Islamabad, collected from February 26, 2020, to September 30, 2021, is analyzed. The ML algorithm is used to optimize the results of ADF, unit root test which ensures the minimum of ACF, and PACF graphs intention of the data series. The results employ the fitted ARIMA models (1, 1, 1) and (1, 1, 7) for the 1st to 4th waves, confirming daily infected cases across the entire dataset of Pakistan. The cumulative trained observations are from the 1st wave (February 26, 2020, to October 20, 2020), 2nd wave (October 21, 2020, to March 16, 2021), 3rd wave (March 17, 2021, to July 10, 2021), and 4th wave (July 11, 2021, to September 30, 2021), with a further 14-day forecast (from October 1 to October 14, 2021). The results show a strong correlation between the trained and predicted values, ranging from 0.8789 to 0.99236. To select predictive model parameters, the model that results in the minimum Bayesian Information Criterion (BIC) value and residuals from the datasets obtained after detaching the unnecessary errors and the 95^% CI for the forecasting error ( ) are calculated. These values would help to decide the best fitted predictive model.

Cooperative energy interaction for neighboring multiple distribution substation areas considering demand response

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA’s load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy.

How Important are Commodity Terms of Trade Shocks in Explaining Government Tax Revenue Fluctuations? Evidence from Ethiopia

Using a new database of commodity terms of trade and identifying country-specific commodity export and import price shocks separately, this article mainly aims to examine the importance of commodity export and import price shocks in explaining government tax revenue fluctuations using Ethiopia as an example over the sample period 1980–2019. This article employs a structural vector autoregressive model and forecast error variance decomposition analysis to address this objective. The results show that commodity export and import price shocks account for around 48% of government tax revenue fluctuations. While commodity export price shocks contribute to around 37% of fluctuations in government tax revenue, commodity import price shocks account for only 11% of government tax revenue fluctuations. These findings are discussed in terms of their policy implications. JEL Classification C32, F41, F44

The Ensemble Tangent Linear Model Applied to a Cloud Physics Parameterization

Abstract An Ensemble Tangent Linear Model (ETLM) is applied to a cloud physics scheme used in the Navy Global Environmental Model (NAVGEM). The ensemble is created using 3-hour forecasts from the Ensemble Transform method used in the NAVGEM data assimilation system. The model states are saved before and after applying the cloud physics parameterization (which includes condensation/evaporation of cloud ice and cloud liquid water and stratiform precipitation), and these states are used to construct linearized model tendencies for temperature, specific humidity, cloud liquid water, and cloud ice water. We examine separately the application of the ETLM to cloud physics components that are explicitly local versus non-local. For the local components, an ETLM is built using a single grid point. ETLMs from 50 to 1000 members are tested, and skillful forecasts can be obtained for both local and non-local physics even with a moderate sized ensemble (e.g., 100 members). At 1000 members, the globally-averaged forecast error reductions (relative to persistence errors) are ∼40% for temperature, water vapor, and cloud liquid water and ∼30% for cloud ice. When initial perturbations are reduced by a factor of 0.1, the error reductions increase to ∼65% for all variables. For physics with non-local components (stratiform precipitation) the covariances that comprise the ETLM are localized with a Schur product matrix using a Gaussian localization shape with tunable length. The optimal lengths increased with ensemble size from ∼2-3 km for 50 members to ∼10 km for 1000 members. ETLMs for “all cloud physics” are also constructed and evaluated.