Weather Observations Research Articles

Pathways to global coordination in space weather: International organizations, initiatives, and space agencies

This paper is the second part of two papers on global coordination in space weather. In this paper the activities of established and emerging international organizations and initiatives related to space weather research and operations are introduced. As shown in the accompanying paper “Global Landscape of Space Weather Observations, Research and Operations”, most of instrumentations for space environment monitoring are operated by each country. On the other hand, it is necessary to integrate the global observation to detect space weather phenomena, and it is essential to coordinate among nations for data sharing, set standard formats and protocols, fill observational gaps, etc. There are multiple international organizations and other actors in the field of space weather, and they have their own purposes and goals. In 2022, three international organizations, WMO, ISES and COSPAR reached an agreement to explore pathways to increased coordination of activities. There is also an emerging movement by various space agencies that fund space weather research missions to establish a forum to share plans and foster discussions leading to possible collaborations that advance understanding and enable progress of space weather operations and applications.

Pengamatan Cuaca Lokal secara Multi Node dengan Internet of Things dan Django Framework

Weather is one of the challenges that humans must experience in their activities. Like the MSMEs of sand crackers in Kediri Regency. This MSME uses sunlight as a means of drying products. What often becomes a problem is unpredictable weather conditions which reduce productivity and quality. Therefore, a real-time local weather observation system is needed to anticipate sudden weather changes. In this research, an IoT-based local weather condition measurement tool will be connected to a system built using the Django framework. This system and tools were tested for eight days. So it was concluded that the system was successfully built with data collection accuracy of 96.31%. Measurements are carried out every 5 minutes or a time frame of 5 minutes. In addition, this system supports observations in several places at once. This multiple node concept is used to detect local weather changes in the surrounding area. So it is not concentrated in the MSME area.

Investigating the potential for students to contribute to climate data rescue: Introducing the Climate Data Rescue Africa project (CliDaR‐Africa)

AbstractThe majority of available climate data in global digital archives consist of data only from the 1940s or 1950s onwards, and many of these series have gaps and/or are available for only a subset of the variables which were actually observed. However, there exist billions of historical weather observations from the 1700s, 1800s, and early 1900s that are still in hard‐copy form and are at risk of being lost forever due to deterioration. An assessment of changes in climate extremes in several IPCC regions was not possible in IPCC AR6 WGI owing, in many cases, to the lack of available data. One such region is Africa, where the climate impact research and the ability to predict climate change impacts are hindered by the paucity of access to consistent good‐quality historical observational data. The aim of this innovative project was to use classroom‐based participatory learning to help transcribe some of the many meteorological observations from Africa that are thus far unavailable to researchers. This project transcribed quickly and effectively station series by enrolling the help of second‐year undergraduate students at Maynooth University in Ireland. The newly digitized African data will increase the temporal and spatial coverage of data in this important data‐sparse region. Students gained new skills while helping the global scientific community unearth new insight into past African climate. The project managed to transcribe 79 months of data at Andapa in Madagascar and 56 months of data for Macenta in Guinea. The digitized data will be openly and freely shared with the scientific and wider community via the Pangaea data repository, the C3S Climate Data Store, and the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) data centre in the US. The project model has the potential for a broader roll‐out to other educational contexts and there is no shortage of data to be rescued. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organizations to instigate similar programs.

Modeling Heat and Water Exchanges between the Atmosphere and an 85-km2 Dimictic Subarctic Reservoir Using the 1D Canadian Small Lake Model

Abstract Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multiyear direct observations of turbulent heat fluxes collected on and around the 85-km2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model that cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error = −0.3 and −2.7 W m−2 for latent and sensible heat fluxes, respectively). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020. Significance Statement Freshwater bodies impact the regional climate through energy and water exchanges with the atmosphere. It is challenging to model surface energy fluxes over a northern lake due to the succession of stratification and mixing periods over a year. This study focuses on the interactions between the atmosphere of an irregular shaped northern hydropower reservoir. Direct measurements of turbulent fluxes using an eddy covariance system allowed the model assessment. Turbulent fluxes were successfully predicted during the open water period. Comparison between observed and modeled time series showed a good agreement; however, the model overreacted to high wind episodes. Biases mostly occur during freeze-up and breakup, stressing the importance of a good representation of the ice cover processes.

Wasted efforts of elite Marathon runners under a warming climate primarily due to atmospheric oxygen reduction

Meteorological conditions exert influences on sport performances via complex processes. Previous studies conventionally investigate the effects of weather conditions on marathon performance by following events held at the same places, which brings large uncertainties due to the changed participants. Via following each athlete to eliminate these uncertainties, we show that elite athletes’ marathon-running performance tends to monotonically worsen as ambient temperature rises except when it is extremely cold based on the best 16 athletes from each of the sex continents. It worsens by 0.56 (0.39 for men and 0.71 for women) min/°C when it rises beyond 15 °C. Theoretical analysis based on global marathon performance and weather observation datasets shows that more than half of this effect is mediated by reduced oxygen partial density as warmer temperature expands air and increases vapor pressure for both the men and women athletes, which is confirmed by the methods of Baron–Kenny. This atmospheric thermodynamic mechanism has not been emphasized previously. We also show that world-top athletes’ marathon performance approximately linearly worsens as ambient pressure decreases and slightly worsens as relative humidity rises if not extremely wet. These results substantially differ from the previous ones following the events instead of each athlete. Multi-variable changes in climate system are projected to slow Olympic marathon athletes by 2.51 and 1.06 min by the end of the 21st century in relative to 2020 under the high and intermediate emission scenario, respectively. Therefore, future progression of marathon performance is likely to be substantially slowed or even halted by climate changes without efficient greenhouse gas mitigation.

SWOL2023: Report of Space Weather Observations Throughout Latinoamerica: Filling the Southern Gaps

AbstractThe “Space Weather Observations throughout Latinoamerica: Filling the Southern gaps” Workshop took place in Ushuaia, Argentina, from 2 to 4 October 2023. This event was exclusively dedicated to Space Weather (SWx), gathering leading international academic and governmental agencies focused on the subject. The meeting addressed the field of SWx from three perspectives: driving phenomena occurring in the solar atmosphere, evolution in the interplanetary environment, and interaction with the Earth. In addition, emphasis was placed on identifying key research directions in solar and space physics that warrant development in Argentina and across the region in the forthcoming years. The workshop also focused on the current observational infrastructure that supports SWx studies, and how to improve and develop it through strategic international partnerships, stressing on the need for real‐time data. The conference fostered discussion among different partners involved in SWx studies, that is, researchers and potential space weather service providers, and the need to include in this debate potential users of space weather services.

Warming climate is helping human beings run faster, jump higher and throw farther through less dense air

Understanding both positive and negative impacts of climate change is essential for comprehensively assessing and well adapting to the impacts of changing climate. Conventionally, climate warming is revealed to negatively impact human activities. Here, we reveal that human beings’ performance in anaerobic sports may benefit from climate warming. Using global weather observation and athletes’ performance datasets, we show that world-top athletes’ performances in nearly all athletics anaerobic events (i.e., sprints, jumps and throws) substantially improve as ambient temperature rises. For example, 100 m performance monotonically improves by 0.26 s as ambient temperature rises from 11.8° to 36.4 °C. Using Coupled Model Intercomparison Project Phase 6 datasets, we further show that global warming can substantially improve world-top athletes’ performance in eleven of the thirteen Olympics athletics anaerobic events by 0.27%–0.88% and 0.14–0.48% under high-emission and medium-emission scenarios, respectively, during 1979–2100. Among them, the improvements for 100 m are 0.59% (0.063 s) and 0.32% (0.034 s), respectively. Mechanism analysis shows that the warmed ambient atmosphere can improve competitors’ performance through expanding the air and thus reducing the air resistance to the competitors and throwing implements for hummer throw and all the sprints, hurdling and jumps. Quantitative analysis estimates that this thermodynamic process is essential for the impacts of warmed ambient atmosphere on the performances in these events as physiological processes are.

The effect of growth, deforestation, forest fires, and volcanoes on Indonesian regional air quality

As a rapidly developing country, Indonesia faces a challenge in improving air quality that is made more difficult by frequent forest fires linked to deforestation and volcanic eruptions. This paper analyses the link between development and air pollution in 30 Indonesian cities from 2002 to 2019. Air quality is measured using two novel regional air quality indicators (AQIs) for PM2.5 and PM10 estimated from weather observations and NASA satellite data on Aerosol Optical Depth (AOD). Results from an Environmental Kuznets Curve (EKC) analysis, based on a dynamic panel data model, show that forest fires and volcanic activity have significantly worsened regional air quality in Indonesia. The model also shows that the EKC hypothesis is rejected for Indonesia as cities have followed a monotonically increasing trend in air pollution with respect to economic growth. An additional 1% of economic growth increased long run pollution as measured by AQI PM2.5 and PM10 by 0.35% and 0.46%, and forest fire events by 0.11% and 0.06%. A partial Benefit-Cost Analysis (BCA) of forest fires shows that particulate matter pollution is excessive. Decreasing the frequency of forest fires by 1% would generate a net benefit of between US$17 million to 145 million. The lower estimate of the health benefits exceeds the agricultural benefits of forest fire without accounting for other non-market costs due to forest fires. Our results imply that, as Indonesia grows economically, air quality is likely to continue deteriorating. Further, regions affected by regular volcanic activity and wildfires should apply tighter emission standards to controllable point source emissions from industry and non-point source emissions such as agricultural forest fires.

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations.

The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O-B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O-B and Gaussian distributions when assimilating all-sky data from FY-4A/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A/AGRI observations and improves the Gaussianity of the O-B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A/AGRI observations in a uniform manner without cloud detection.

Empirical modeling of agricultural climate risk

Effective policies for adaptation to climate change require understanding how impacts are related to exposures and vulnerability, the dimensions of the climate system that will change most and where human impacts will be most draconian, and the institutions best suited to respond. Here, we propose a simple method for more credibly pairing empirical statistical damage estimates derived from recent weather and outcome observations with projected future climate changes and proposed responses. We first analyze agricultural production and loan repayment data from Brazil to understand vulnerability to historical variation in the more predictable components of temperature and rainfall (trend and seasonality) as well as to shocks (both local and over larger spatial scales). This decomposed weather variation over the past two decades explains over 50% of the yield variation in major Brazilian crops and, critically, can be constructed in the same way for future climate projections. Combining our estimates with bias-corrected downscaled climate simulations for Brazil, we find increased variation in yields and revenues (including more bad years and worse outcomes) and higher agricultural loan default at midcentury. Results in this context point to two particularly acute dimensions of vulnerability: Intensified seasonality and local idiosyncratic shocks both contribute to worsening outcomes, along with a reduced capacity for spatially correlated ("covariate") shocks to ameliorate these effects through prices. These findings suggest that resilience strategies should focus on institutions such as water storage, financial services, and reinsurance.

Application of Fuzzy Logic to Predict Rice Production Quantity in Bogor Regency

This study aims to understand prediction methods using fuzzy Mamdani logic to conclude the quantity of rice production in the Bogor Regency area. Then in this study, we used quantitative methods and involved interviews with resource persons, namely rice mill workers and farmers. The data obtained from the interview is then fuzzyfication to be used in calculations. The main finding of this study is the elaboration of crop quantity from small, normal, and many, with the range of yields produced ranging from 0 to 3000 KG for little, 3000 to 6000 KG for normal, and 6000 to 10000 KG for many. Weather and humidity observations were found to significantly affect rice yields. The implications of these findings are improved accuracy in yield estimation, and the ability to address uncertainty and complexity in factors affecting crop growth. Despite the limitations of this study, such as limited data and remote study locations, the use of fuzzy logic can make a crucial contribution to determining the quantity of water needed for plant growth based on rainfall and weather conditions. This research is crucial because it can be a reference for future research that uses fuzzy logic to determine the quantity of rice production.

Investigating the economic impact of climate change on agriculture in Iran: Spatial spillovers matter

AbstractIn this study, we enhance our understanding of the economic impacts of climate change on agriculture in Iran to provide further information for moving Iran's climate policy forward by linking farmland net revenue to novel climatic and non‐climatic variables. We take advantage of spatial panel econometrics to better circumvent omitted factors extraneous to the agricultural sector and to develop a more reliable and consistent model when data are inherently spatial. In contrast to conventional panel studies which relied on year‐to‐year weather observations, we exploit a hybrid approach to compromise between the disadvantages and advantages of longer‐term cross‐sectional analysis and shorter‐term panel models. We estimate the potential impacts of climate change on agriculture under several global warming scenarios based on the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). We find that (I) farmlands’ net revenues are projected to decline by 8%–19% and 14%–51% by 2050 and 2080; (II) the distributional impacts of climate change would highly depend on climate zones and geographical locations; (III) a few counties might benefit from climate changes; (IV) finally, failing to account for spatial spillovers when they are present leads to a misspecified model.

Hyper‐Local Temperature Prediction Using Detailed Urban Climate Informatics

AbstractThe accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics.

Ice Crystal Alignment: A Concept for High-Temporal-Resolution Monitoring Using Polarimetric Phased Array Radar

Abstract This article describes a concept whereby future operational polarimetric phased array radars (PPAR) routinely monitor ice crystal alignment regions caused by thundercloud electric fields with volume scan updates (∼12 min−1) sufficient to resolve the temporal variation due to lightning and subsequent rapid electric field regeneration in nonsevere thunderstorms. Routine observations of crystal alignment regions may enhance thunderstorm nowcasting through comparison of their temporal and spatial structure with other polarimetric signatures, integration with lightning detection data, and assimilation into convection resolving numerical weather prediction models. If crystal alignment observations indicate strong electrification well in advance of the first lightning strike and likewise reliably indicate the decay of strong electric fields at the end of a storm, this capability may improve warning for lightning-sensitive activities such as airport ramp operations and space launch. Experimental observations of crystal alignment volumes in central Oklahoma severe storms and their relation to those storms’ structures are presented and used to motivate discussion of possible PPAR architectures. In one case—a tornadic supercell—these observations illustrate an important limitation. Even the hypothesized 12 min−1 volume scan update rate would not resolve the temporal variation of the crystal alignment regions in such storms, suggesting that special, adaptive scanning methods may be appropriate for such storms. We describe how future operational phased array radars could support a crystal alignment measurement mode via parallel, time-multiplexed processing and discuss potential impacts on the radar’s primary weather observation mission. We conclude by discussing research needed to better understand technical challenges and operational benefits.

Sea breeze-driven effects on wind down-ramps: Implications for wind farms along the north-east coast of Brazil

This research focuses on understanding a meteorological phenomenon that is related to operational disruptions in a wind power plant on the North-East coast of Brazil. The study employed enhanced weather observations based on SODAR and LIDAR wind profilers, micrometeorological masts, ERA-5 reanalysis data, wind power production data from Brazilian power grid operators, and GOES-16 thermal and visible imagery. On August 15, 2023, Brazil experienced a widespread blackout, which was largely caused by down-ramp events that affected several wind farms at the same time. The factors leading to the phenomenon were categorized into three driven-dependent categories: i) a sharp fall in dew-point temperature, ii) the presence of a cumulus cloud line just above the coastline in the early morning, and/or iii) rainfall over the adjoining sea, providing the feasibility to nowcasting this severe wind ramp. The results derived from the conceptual model suggest that sea-breezes play a significant role in the net wind at the turbine hub-height, and often exceed the influence of large-scale flow. The net wind is considered to be caused by the interplay between trade winds and consistent breezes. It is hoped this research can provide a valuable insight into the complex interactions between local meteorological phenomena and wind energy production on the North-East coast of Brazil, and that its application has the potential to improve the operational forecasting of wind power plants.

Spatial and Temporal Variations of Temperature in the Punjab Province, Pakistan

Identifying the temperature change at a regional level is one of the essential parameters to determine theintensity of climate change. The current investigation provides an examination of changing trends of temperature in thePunjab province from 1970 to 2019. Sen's slope estimator method is applied to monthly data of mean temperature(Tmean), maximum temperature (Tmax), and minimum temperature (Tmin) to calculate the rate of temperature change.Statistical methods were used to find out the level of significance in terms of negative or positive trends to examine thevariability among various weather observatories. Moreover, predicted values have also been observed for a detailedanalysis of temperature variability and trends. Significant and pronounced changes in the mean temperature (T mean)are distinguished all over the Punjab regions with an increasing trend from North to South Punjab. In the case ofmaximum temperature (Tmax), a faster rate of rising in temperature is observed over the Southern and Western regionsof Punjab. In contrast, the minimum temperature (Tmin) shows an increasing trend in Central Punjab. The findingsprovide detailed insight to policymakers for the planning of mitigating efforts and adaptation strategies in response toclimate change.

Developing a statistical approach of evaluating daily maximum and minimum temperature observations from third‐party automatic weather stations in Australia

AbstractThird‐party automatic weather stations (TPAWS) provide a compelling data source for scientists and practitioners to observe and estimate more accurate fine‐scale atmospheric conditions, including daily maximum and minimum temperature (denoted as Tmax and Tmin, respectively), than the current primary weather observation network can offer. Several uncertainties and errors arise in data from TPAWS as the quality control applied to these stations may be inadequate or ad hoc. In this study, we develop a statistical approach to evaluate the quality of daily Tmax and Tmin observations collected from TPAWS in Australia. Our approach compares a target observation with multiple types of reliable reference data, including neighbouring primary weather observations from the official Bureau of Meteorology of Australia stations, Australian Gridded Climate Data, and numerical weather prediction data. Guided by the operational requirements in terms of automation, interpretability, and simplicity as well as expandability, a separate test is formed for each type of reference data and then all the individual tests are merged to generate a single result based on a Gaussian mixture model that is used to provide the final overall assessment for each TPAWS observation. The overall assessment is made in the form of a p‐value‐based confidence score that measures the difference between the target observation and trusted reference data. Our method is validated by synthetic datasets based on high‐quality observations and is also applied to daily Tmax and Tmin observations from 184 TPAWS owned by the Department of Primary Industries and Regional Development of Western Australia. The framework can be readily applied to different regions with different reliable or trusted data sources.

Digitizing observations from the 1861–1875 Met Office Daily Weather Reports using citizen scientist volunteers

AbstractWe describe the transcription and quality control processes for rescuing around 570,000 sub‐daily and daily weather observations which were recorded in the UK Met Office Daily Weather Reports during the 1861–1875 period. These data are from the start of coordinated weather observations and were collected with the aim of making the first‐ever weather forecasts. The observations were rescued thanks to 3500 volunteers and include sub‐daily sea‐level pressure, dry and wet bulb temperatures, daily maximum and minimum temperatures, and daily rainfall amounts from 70 different locations across Western Europe, and one in Canada. We highlight how these observations will be used to fill gaps in existing pressure and temperature datasets and use two case studies to show how the pressure observations will likely better constrain the atmospheric circulation during two severe storms. We also compare a sub‐sample of the newly rescued observations with data that were previously digitized for a small number of locations for the same dates, finding good agreement in general, although some discrepancies remain.

Sparse Grid Imputation Using Unpaired Imprecise Auxiliary Data: Theory and Application to PM2.5 Estimation

Sparse grid imputation (SGI) is a challenging problem, as its goal is to infer the values of the entire grid from a limited number of cells with values. Traditionally, the problem is solved using regression methods such as KNN and kriging, whereas in the real world, there is often extra information—usually imprecise—that can aid inference and yield better performance. In the SGI problem, in addition to the limited number of fixed grid cells with precise target domain values, there are contextual data and imprecise observations over the whole grid. To solve this problem, we propose a distribution estimation theory for the whole grid and realize the theory via the composition architecture of the Target-Embedding and the Contextual CycleGAN trained with contextual information and imprecise observations. Contextual CycleGAN is structured as two generator–discriminator pairs and uses different types of contextual loss to guide the training. We consider the real-world problem of fine-grained PM2.5 inference with realistic settings: a few (less than 1%) grid cells with precise PM2.5 data and all grid cells with contextual information concerning weather and imprecise observations from satellites and microsensors. The task is to infer reasonable values for all grid cells. As there is no ground truth for empty cells, out-of-sample mean squared error and Jensen–Shannon divergence measurements are used in the empirical study. The results show that Contextual CycleGAN supports the proposed theory and outperforms the methods used for comparison.

Evaluation of in situ observations on Marine Weather Observer during Typhoon Sinlaku

Abstract. The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper.