Forecast Center Research Articles

Process‐oriented stochastic perturbations applied to the parametrization of turbulence and shallow convection for ensemble prediction

AbstractWeather forecasting nowadays often requires some estimation of uncertainties associated with the output of meteorological models, in order to better inform decision making, especially in the context of intense weather events. Ensemble prediction systems provide such information through sets of possible scenarios which are designed to represent various uncertainty sources, including model uncertainties. A wide variety of methods have been proposed to estimate model uncertainties, among which perturbation methods targeting uncertain processes are a promising research field. In this study, we focus on the representation of small‐scale variability by process‐oriented perturbation schemes applied to two key physical processes, namely turbulence and shallow convection. The perturbations are applied to a single‐column version of the convection‐permitting AROME model, in three idealized boundary‐layer cases. Large‐eddy simulations (LESs) of the same cases serve as a reference for the subgrid variability that has to be represented, and the results are also compared to those given by the Stochastically Perturbed Parametrization Tendencies (SPPT) method, which is a method commonly used by weather forecast centres to represent model uncertainty. The spread produced by our process‐oriented perturbations of turbulence and shallow convection does not represent all the small‐scale variability implied by the LESs for temperature and humidity. However, it is of a similar order of magnitude for the wind, thanks to perturbations generated by the stochastic turbulence scheme. The dispersion is structurally different from what is obtained with SPPT. It is non‐negligible in the lower levels, where SPPT perturbations are usually suppressed because of numerical instabilities, indicating a possible complementarity between the schemes.

Investigation of Machine Learning Using Satellite-Based Advanced Dvorak Technique Analysis Parameters to Estimate Tropical Cyclone Intensity

AbstractSeveral simple and computationally inexpensive machine learning models are explored that can use advanced Dvorak technique (ADT)-retrieved features of tropical cyclones (TCs) from satellite imagery to provide improved maximum sustained surface wind speed (MSW) estimates. ADT (version 9.0) TC analysis parameters and operational TC forecast center best track datasets from 2005 to 2016 are used to train and validate the various models over all TC basins globally and select the best among them. Two independent test sets of TC cases from 2017 to 2018 are used to evaluate the intensity estimates produced by the final selected model called the “artificial intelligence (AI)” enhanced advanced Dvorak technique (AiDT). The 2017–18 MSW results demonstrate a global RMSE of 7.7 and 8.2 kt (1 kt ≈ 0.51 m s−1), respectively. Basin-specific MSW RMSEs of 8.4, 6.8, 7.3, 8.0, and 7.5 kt were obtained with the 2017 dataset in the North Atlantic, east/central Pacific, northwest Pacific, South Pacific/south Indian, and north Indian Ocean basins, respectively, with MSW RMSE values of 8.9, 6.7, 7.1, 10.4, and 7.7 obtained with the 2018 dataset. These represent a 30% and 23% improvement over the corresponding ADT RMSE for the 2017–18 datasets, respectively, with the AiDT error reduction significant to 99% in both sets. The AiDT model represents a notable improvement over the ADT performance and also compares favorably to more computationally expensive and complex machine learning models that interrogate satellite images directly while still preserving the operational familiarity of the ADT.

Properties of Saharan Dust Versus Local Urban Dust—A Case Study

AbstractOn June 29–30, 2019, the Barcelona Dust Forecast Center with Non‐hydrostatic Multiscale Model (NMMB/BSC‐Dust) and the Navy Aerosol Analysis and Prediction System forecasted huge amounts of mineral dust over Poland. The Hybrid Single Particle Lagrangian Integrated Trajectory model confirmed uniquely fast (120 hr) long‐range air‐mass transport form North Africa to Poland. This remarkable dust event was observed using lidar at the Aerosol, Clouds and Trace Gases Research InfraStructure site in Warsaw, Central Poland; the only site equipped with Raman‐Mie polarization water vapor lidar in East‐Central Europe. The excellent capabilities of PollyXT lidar allowed to obtain an impressive number of 31 full sets of aerosol optical properties profiles, which enabled study of dust properties evolution on a rare hourly scale. The analyses were completed with the separation of fine and coarse mode dust particles form non‐dust particles using the POlarization‐LIdar PHOtometer Networking algorithm. Huge amount of an exceptionally pure mineral dust from Sahara measured in the free troposphere was characterized by a gradually decreasing coarse dust fraction (76%–21%) with a peak of fine dust fraction (67%) and particle linear depolarization ratio (26%) in the middle of the event. Within the boundary layer, a local urban dust mixed with pollution was observed with fine mode dust particles dominating (44%) and lower particle linear depolarization ratio (7.4%). The influx of pure mineral dust has been unique to this geographical region and will therefore be a reference point for future research and comparative studies.

PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μg/m3, and a root mean square error (RMSE) of 5.8 μg/m3. This study also found that the model performance as well as the site measured PM2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R2) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R2 scores correlate positively with the mean measured PM2.5 concentration at monitoring sites. Mean measured PM2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM2.5 concentrations. In addition, the reconstructed PM2.5 surfaces serve as an important data source for pollution event tracking and PM2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM2.5 estimates were made for 10 km by 10 km and the PM2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM2.5 concentrations.

Assimilation of Ground and Satellite Snow Observations in a Distributed Hydrologic Model for Water Supply Forecasting

AbstractRunoff from the Upper Colorado River Basin (UCRB) is an important water resource in the western United States. The majority of annual runoff is derived from spring snowmelt, and therefore April–July water supply volume (WSV) forecasts produced by the Colorado Basin River Forecast Center are critical to basin water management. The primary objective of this study was to evaluate the impact of snow data assimilation (DA) in a distributed hydrologic model on WSV forecasting error and skill in headwater catchments of the UCRB. To do this, a framework was built to use the Localized Ensemble transform Kalman filter to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory‐Research Distributed Hydrologic Model with SNOTEL SWE observations and spatially and temporally complete MODIS Snow Covered‐Area and Grain size data. The DA approach was assessed by evaluating ensemble streamflow prediction forecasts over a 20‐year period for 23 catchments in the UCRB. Overall, the DA approach improved water supply forecast skill in 80% of pilot basins with an average improvement in the median continuous rank probability skill score of 4%. A research‐to‐operations transition was facilitated by automating the implementation of the DA approach. This work demonstrates the capacity for gridded and point snow products to be used to objectively update model states in an operational forecasting setting.

Intercomparison of gridded precipitation datasets for prospective hydrological applications in Sri Lanka

AbstractThe aim of this study was to assess the application of gridded precipitation datasets for applications in Sri Lanka and to identify suitable products that can be used for hydrological applications in data sparse regions. Here, nine global datasets were considered for this study, and after initial screening, four datasets, such as Integrated Multi‐Satellite Retrievals for GPM (IMERG) early run calibrated, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), European Center for Medium range Weather Forecasting (ECMWF)—ReAnalysis (ERA5) and Indian Monsoon Data Assimilation and Reanalysis (IMDAA), were selected for detailed comparison. These data products were analysed at monthly, seasonal and annual time scale for whole Sri Lanka and also for three distinguished climate zones (wet, intermediate and dry) for the period 2001–2015. The datasets were further evaluated based on their ability to capture the extreme events, based on recent designated dry year and wet year. All four datasets represent the annual and monthly climatology of Sri Lanka well; however, discrepancies are seen at seasonal scale. CHIRPS had the best performance in the wet zone and ERA5 in dry and intermediate zones. Although all datasets distinctly identified the wet year and dry year with respect to the normal average, the interannual variability was explicitly identified by CHIRPS and ERA5. Therefore, for replicating the national and zonal climatology, both ERA5 (dry and intermediate zones) and CHIRPS (wet zone) are suitable. With the perspective of hydrological applications, both datasets can be used in development of hydrological models for applications such as flood forecasting, reservoir management and climate change impact assessment as an alternative to observed rainfall data in data scarce regions.

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

We present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

Compressing atmospheric data into its real information content

Hundreds of petabytes are produced annually at weather and climate forecast centers worldwide. Compression is essential to reduce storage and to facilitate data sharing. Current techniques do not distinguish the real from the false information in data, leaving the level of meaningful precision unassessed. Here we define the bitwise real information content from information theory for the Copernicus Atmospheric Monitoring Service (CAMS). Most variables contain fewer than 7 bits of real information per value and are highly compressible due to spatio-temporal correlation. Rounding bits without real information to zero facilitates lossless compression algorithms and encodes the uncertainty within the data itself. All CAMS data are 17× compressed relative to 64-bit floats, while preserving 99% of real information. Combined with four-dimensional compression, factors beyond 60× are achieved. A data compression Turing test is proposed to optimize compressibility while minimizing information loss for the end use of weather and climate forecast data.

Современные методы навигационного мониторинга и позиционирования сельскохозяйственной техники

The article considers the state of transport monitoring systems used in agricultural production. The urgency of the problem is explained by the need to increase the profitability of enterprises of the agricultural complex. An effective way to solve the problem is strict control of the operation of transport used in the technological process of agricultural production in order to optimize and increase the efficiency of its operation. The direct dependence of the profitability of an agricultural enterprise on the monitoring of transport is considered and justified. To illustrate, the results of a survey conducted in 2020 by the staff of the Center for Forecasting and Monitoring of the Kuban State Agrarian University are used. It is concluded that monitoring systems make it possible to rationalize the process of cultivating arable land and significantly reduce fuel costs, and agricultural enterprises that do not use digital transformation technologies lose out in the competition to more modern companies. Keywords: AGRICULTURAL PRODUCTS, SATELLITE COMMUNICATIONS, MONITORING, POSITIONING, TRANSPORT, AGRICULTURE, GLONASS, GPS

Using Conditional Nonlinear Optimal Perturbation to Generate Initial Perturbations in ENSO Ensemble Forecasts

AbstractUsing the latest operational version of the ENSO forecast system from the National Marine Environmental Forecasting Center (NMEFC) of China, ensemble forecasting experiments are performed for El Niño-Southern Oscillation (ENSO) events that occurred from 1997 to 2017 by generating initial perturbations of the conditional nonlinear optimal perturbation (CNOP) and Climatically relevant Singular Vector (CSV) structures. It is shown that when the initial perturbation of the leading CSV structure in the ensemble forecast of the CSVs-scheme is replaced by those of the CNOP structure, the resulted ensemble ENSO forecasts of the CNOP+CSVs-scheme tend to possess a larger spread than the forecasts obtained with the CSVs-scheme alone, leading to a better match between the root mean square error and the ensemble spread, a more reasonable Talagrand diagram and an improved Brier skill score (BSS). All these results indicate that the ensemble forecasts generated by the CNOP+CSVs-scheme can improve both the accuracy of ENSO forecasting and the reliability of the ensemble forecasting system. Therefore, ENSO ensemble forecasting should consider the effect of nonlinearity on the ensemble initial perturbations to achieve a much higher skill. It is expected that fully nonlinear ensemble initial perturbations can be sufficiently yielded to produce ensemble forecasts for ENSO, finally improving the ENSO forecast skill to the greatest possible extent. The CNOP will be a useful method to yield fully nonlinear optimal initial perturbations for ensemble forecasting.

Updated System for the Sea Wave Operational Forecast of the Black Sea Marine Forecasting Center

Purpose. The work is aimed at updating the sea wave forecasting system developed in the Black Sea Marine Forecasting Center by including the block of wind wave forecast in the Sevastopol region and by improving the wave forecast accuracy using the proposed procedure for the SWAN model tuning. Methods and Results. In the updated forecasting system, the possibility of performing the joint operational sea wave forecasts for the Black Sea and the Sevastopol region (with the 5 and 1 km spatial resolutions, respectively) became possible due to the nested grid method applied. To improve accuracy of the wave forecasts, the procedure for the SWAN model tuning was proposed. It is based on changing the parameterization of the surface friction coefficient Cd(V), where V is the surface wind speed. This permits to reduce the deviations of the forecasted wave heights from those obtained from the satellite altimetry measurements. Efficiency of the proposed procedure was assessed through comparison of the forecasting results with the remote sensing data. It is shown that in the forecasts supplied with an optimal choice of functional dependence Cd(V), the scattering index between the forecasted and measured values can be reduced by 20 %. Conclusions. Represented is the updated system of the Black Sea Marine Forecasting Center intended for the joint operational sea wave forecasts in the Black Sea and in the Sevastopol region. The results of model validation have shown that the procedure proposed for tuning the SWAN model makes it possible to reduce the deviations of the forecasted wave heights from those measured by the sensors installed at the altimetry satellites.

Modernizirovannaya sistema operativnogo prognoza morskogo volneniya Chernomorskogo tsentra morskikh prognozov

Purpose. The work is aimed at updating the sea wave forecasting system developed in the Black Sea Marine Forecasting Center by including the block of wind wave forecast in the Sevastopol region and by improving the wave forecast accuracy using the proposed procedure for the SWAN model tuning. Methods and Results. In the updated forecasting system, the possibility of performing the joint operational sea wave forecasts for the Black Sea and the Sevastopol region (with the 5 and 1 km spatial resolutions, respectively) became possible due to the nested grid method applied. To improve accuracy of the wave forecasts, the procedure for the SWAN model tuning was proposed. It is based on changing the parameterization of the surface friction coefficient 𝐶𝑑(𝑉), where V is the surface wind speed. This permits to reduce the deviations of the forecasted wave heights from those obtained from the satellite altimetry measurements. Efficiency of the proposed procedure was assessed through comparison of the forecasting results with the remote sensing data. It is shown that in the forecasts supplied with an optimal choice of functional dependence 𝐶𝑑(𝑉), the scattering index between the forecasted and measured values can be reduced by 20 %. Conclusions. Represented is the updated system of the Black Sea Marine Forecasting Center intended for the joint operational sea wave forecasts in the Black Sea and in the Sevastopol region. The results of model validation have shown that the procedure proposed for tuning the SWAN model makes it possible to reduce the deviations of the forecasted wave heights from those measured by the sensors installed at the altimetry satellites.

Estimating tropical cyclone surface winds: Current status, emerging technologies, historical evolution, and a look to the future

This article provides a review of tropical cyclone (TC) surface wind estimation from an operational forecasting perspective. First, we provide a summary of operational forecast center practices and historical databases. Next, we discuss current and emerging objective estimates of TC surface winds, including algorithms, archive datasets, and individual algorithm strengths and weaknesses as applied to operational TC surface wind forecast parameters. Our review leads to recommendations about required surface coverage – an area covering at least 1100 km from center of TC at a 2-km resolution in the inner-core, and at a frequency of at least once every 6 h. This is enough coverage to support a complete analysis of the TC surface wind field from center to the extent of the 34-kt (17 m s−1) winds at 6-h intervals. We also suggest future designs of TC surface wind capabilities include funding to ensure near real-time data delivery to operators so that operational evaluation and use are feasible within proposed budgets. Finally, we suggest that users of archived operational wind radii datasets contact operational organizations to ensure these datasets are appropriate for their needs as the datasets vary in quality through time and space, even from a single organisation.

Applicability of the M5 to Forecasting at Walmart

The M5 Forecasting Competition, the fifth in the series of forecasting competitions organized by Professor Spyros Makridakis and the Makridakis Open Forecasting Center at the University of Nicosia, was an extremely successful event. This competition focused on both the accuracy and uncertainty of forecasts and leveraged actual historical sales data provided by Walmart. This has led to the M5 being a unique competition that closely parallels the difficulties and challenges associated with industrial applications of forecasting. Like its precursor the M4, many interesting ideas came from the results of the M5 competition which will continue to push forecasting in new directions.In this article we discuss four topics around the practitioners view of the application of the competition and its results to the actual problems we face. First, we examine the data provided and how it relates to common difficulties practitioners must overcome. Secondly, we review the relevance of the accuracy and uncertainty metrics associated with the competition. Third, we discuss the leading solutions and their implications to forecasting at a company like Walmart. We then close with thoughts about a future M6 competition and further enhancements that can be explored.

Evaluation of Global Wave Probabilities Consistent with Official Forecasts

AbstractThe U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12-ft and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20x20 degree latitude longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability-based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12-ft and 18-ft significant wave events and non-events. The verification statistics also shows that the wind probability-based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.

Use of the DBM Model to the Predict of Arrival of Coronal Mass Ejections to the Earth

This paper analyzes the results of modeling coronal mass ejection (CME) propagation in 2010–2011 obtained using input data from different sources: CME catalogs SEEDS and CACTus, and predictions of the velocity of quasi-stationary solar wind fluxes, as an environment, through which CMEs propagate. As the model of quasi-stationary solar wind fluxes, the model for predicting the velocity of the solar wind of the Space Weather Forecast Center of the Skobeltsyn Institute of Nuclear Physics of Moscow State University, operating online, is used. The CME prediction is carried out using the Simple Drag-Based Model. A comparison was performed between the ICME arrival time and their velocities obtained when modeling with data from the open ICME catalogs: the Richardson and Cane ICME catalog and the GMU CME List. Based on the comparison, it was concluded that a more accurate prediction for the growth phase of the 24th solar activity cycle was obtained using data on CME parameters from the CACTus database. The obtained errors in predicting the ICME parameters are comparable with the errors of other existing models.

Important factors in the tracking of tropical cyclones in operational models

AbstractMultiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

RMetS Meteorological Masterclass Series: Anticipating floods, droughts and heatwaves

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Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs

More high-quality, in situ observations of essential marine variables are needed over the seasonal ice zone to better understand Arctic (or Antarctic) weather, climate, and ecosystems. To better assess the potential for arrays of uncrewed surface vehicles (USVs) to provide such observations, five wind-driven and solar-powered saildrones were sailed into the Chukchi and Beaufort Seas following the 2019 seasonal retreat of sea ice. They were equipped to observe the surface oceanic and atmospheric variables required to estimate air-sea fluxes of heat, momentum and carbon dioxide. Some of these variables were made available to weather forecast centers in real time. Our objective here is to analyze the effectiveness of existing remote ice navigation products and highlight the challenges and opportunities for improving remote ice navigation strategies with USVs. We examine the sources of navigational sea-ice distribution information based on post-mission tabulation of the sea-ice conditions encountered by the vehicles. The satellite-based ice-concentration analyses consulted during the mission exhibited large disagreements when the sea ice was retreating fastest (e.g., the 10% concentration contours differed between analyses by up to ∼175 km). Attempts to use saildrone observations to detect the ice edge revealed that in situ temperature and salinity measurements varied sufficiently in ice bands and open water that it is difficult to use these variables alone as a reliable ice-edge indicator. Devising robust strategies for remote ice zone navigation may depend on developing the capability to recognize sea ice and initiate navigational maneuvers with cameras and processing capability onboard the vehicles.

Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs.

More high-quality, in situ observations of essential marine variables are needed over the seasonal ice zone to better understand Arctic (or Antarctic) weather, climate, and ecosystems. To better assess the potential for arrays of uncrewed surface vehicles (USVs) to provide such observations, five wind-driven and solar-powered saildrones were sailed into the Chukchi and Beaufort Seas following the 2019 seasonal retreat of sea ice. They were equipped to observe the surface oceanic and atmospheric variables required to estimate air-sea fluxes of heat, momentum and carbon dioxide. Some of these variables were made available to weather forecast centers in real time. Our objective here is to analyze the effectiveness of existing remote ice navigation products and highlight the challenges and opportunities for improving remote ice navigation strategies with USVs. We examine the sources of navigational sea-ice distribution information based on post-mission tabulation of the sea-ice conditions encountered by the vehicles. The satellite-based ice-concentration analyses consulted during the mission exhibited large disagreements when the sea ice was retreating fastest (e.g., the 10% concentration contours differed between analyses by up to ~175 km). Attempts to use saildrone observations to detect the ice edge revealed that in situ temperature and salinity measurements varied sufficiently in ice bands and open water that it is difficult to use these variables alone as a reliable ice-edge indicator. Devising robust strategies for remote ice zone navigation may depend on developing the capability to recognize sea ice and initiate navigational maneuvers with cameras and processing capability onboard the vehicles.