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Shuaibing Shao
,
Xin-Min Zeng
,
Ning Wang
,
Irfan Ullah
, and
Haishen Lv

Abstract

Currently, there is a lack of investigating moisture sources for precipitation over the upstream catchment of the Three Gorges Dam (UCTGD), the world’s largest dam. Using the dynamical recycling model (DRM), trajectory frequency method (TFM), and the Climate Forecast System Reanalysis (CFSR), this study quantifies moisture sources and transport paths for UCTGD summer precipitation from 1980 to 2009 based on two categories of sources: region-specific and source-direction. Overall, the land and oceanic sources contribute roughly 63% and 37%, respectively, of the moisture to UCTGD summer precipitation. UCTGD and the Indian Ocean are the most important land and oceanic sources, respectively, in which the southern Indian Ocean with over 10% of moisture contribution was overlooked previously. Under the influence of the Asian monsoon and prevailing westerlies, the land contribution decreases to 57.3% in June, then gradually increases to 68.8%. It is found that for drought years with enhanced southwest monsoon, there is a weakening of the moisture contribution from the C-shaped belt along the Arabian Sea, South Asia, and UCTGD, and vice versa. TFM results show three main moisture transport paths and highlight the importance of moisture from the southwest. Comparison analysis indicates that, generally, sink regions are more affected by land evaporation with their locations more interior to the center of the mainland. Furthermore, correlations between moisture contributions and indices of general circulation and sea surface temperature are investigated, suggesting that these indices affect precipitation by influencing moisture contributions of the subregions. All of these are useful for comprehending the causes of summer UCTGD precipitation.

Significance Statement

Quantitative research on the moisture sources of summer precipitation has been implemented for the upstream catchment of the Three Gorges Dam (UCTGD), which is of particular hydrological significance but has not been investigated previously. The dynamical recycling model (DRM)–trajectory frequency method (TFM) approach is used to quantify and interpret the results of the moisture sources both in different specific subregions and directions, which produce more meaningful results than a single method for the areal division of moisture sources. Furthermore, antecedent indices that significantly influence the following moisture contributions of the subregions and then summer UCTGD precipitation are studied in terms of large-scale general circulation indices, which would help our understanding of precipitation forecast for UCTGD.

Restricted access
Marcin Paszkuta
,
Maciej Markowski
, and
Adam Krężel

Abstract

Empirical verification of the reliability of estimating the amount of solar radiation entering the sea surface is a challenging topic due to the quantity and quality of data. The collected measurements of total and diffuse radiation from the Multifilter Rotating Shadowband Radiometer (MRF-7) commercial device over the Baltic Sea were compared with the satellite results of using modeling data. The obtained results, also divided into individual spectral bands, were analyzed for usefulness in satellite cloud and aerosol detection. The article presents a new approach to assessing radiation and cloud cover based on the use of models supported by satellite data. Measurement uncertainties were estimated for the obtained results. To reduce uncertainty, the results were averaged to the time constant of the device, day, and month. The effectiveness of the method was determined by comparison against the SM Hel measurement point. The empirical results obtained confirm the effectiveness of using satellite methods for estimating radiation along with cloud-cover detection over the sea with the adopted uncertainty values.

Significance Statement

The difference in the amount of solar energy reaching the sea surface between cloudless and cloudy areas reaches tens of percent. Empirical results confirm the effectiveness of using satellite methods to estimate solar radiation along with cloud-cover detection. Over the sea in comparison to land, the amount of empirical data is limited. This research uses new empirical results of radiation to determine the accuracy of satellite estimation results. Experimental results show that the proposed method is effective and adequately parameterizes the detection of satellite image features.

Open access
Enrico Chinchella
,
Arianna Cauteruccio
, and
Luca G. Lanza

Abstract

The measurement accuracy of an electroacoustic precipitation sensor, the Vaisala WXT520, is investigated to quantify the associated wind-induced bias. The device is widely used as a noncatching tool for measuring the integral features of liquid precipitation, specifically rainfall amount and intensity. A numerical simulation using computational fluid dynamics is used to determine the bluff-body behavior of the instrument when exposed to wind. The obtained airflow velocity patterns near the sensor are initially validated in a wind tunnel. Then, the wind-induced deviation and acceleration/deceleration of individual raindrop trajectories and the resulting impact on the measured precipitation are replicated using a Lagrangian particle tracking model. The sensor’s specific measurement principle necessitates redefining catch ratios and the collection efficiency in terms of the resulting kinetic energy and quantifying them as a function of particle Reynolds number and precipitation intensity, respectively. Wind speed and direction and drop size distribution have been simulated across various combinations. The results show that the measured precipitation is overestimated by up to 400% under the influence of wind. The presented adjustment curves can be used to correct raw rainfall measurements taken by the Vaisala WXT520 in windy conditions, either in real time or as a postprocessing function. The magnitude of the adjustment at any operational aggregation level largely depends on the local rainfall and wind regimes at the site of measurement and may have a strong impact on applications in regions where wind is frequent during low- to medium-intensity precipitation.

Restricted access
Shengjun Liu
,
Wenjie Yan
,
Xinru Liu
,
Yamin Hu
, and
Dangfu Yang

Abstract

The research and application of convolutional neural networks (CNNs) on statistical downscaling have been hampered by the fact that deep learning is highly dependent on sample size and is considered to be a black-box model. Therefore, a CNN model with transfer learning (CNN-TL) is proposed to study the pre-rainy season precipitation of South China. First, an augmented monthly dataset is created by sliding a fixed-length window over the daily circulation field and precipitation data for the entire year. Next, a base CNN network is pretrained on the augmented dataset, and then the network parameters are tuned on the actual monthly dataset from South China. Then, guided backpropagation is conducted to obtain the distribution regions of the key features and explain the net. The coefficient of determination R 2 and root-mean-square error (RMSE) show that the CNN-TL model has higher explanatory power and better fitting performance than the feature extraction–based random forest. In comparison with the base CNN, the transfer learning approach can improve the explanatory power of the model by 10.29% and reduce the average RMSE by 6.82%. In addition, the interpretation results of the model show that the critical regions are primarily South China and its surrounding areas, including the Indochina Peninsula, the Bay of Bengal, and the South China Sea. Furthermore, the ablation experiments and composite analysis illustrate that these regions are very important.

Significance Statement

To mitigate the challenges posed by small sample sizes and the transparency of deep learning in downscaling problems, we propose a convolutional neural network based on sample augmentation and transfer learning to study the monthly precipitation downscaling problem during the preflood period in South China. In comparison with random forests and conventional convolutional neural networks, our model achieves an optimal interpretation rate and stability. In addition, we explore the interpretability of the model using guided backpropagation to find the distribution of key features within the large-scale circulation field, thus increasing the credibility of the model.

Restricted access
Joseph S. Schlosser
,
Ryan Bennett
,
Brian Cairns
,
Gao Chen
,
Brian L. Collister
,
Johnathan W. Hair
,
Michael Jones
,
Michael A. Shook
,
Armin Sorooshian
,
Kenneth L. Thornhill
,
Luke D. Ziemba
, and
Snorre Stamnes

Abstract

Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud Meteorology Interactions Over the western Atlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically resolved lidar + polarimeter–derived aerosol particle number concentration (Na ) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest-neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open-source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms.

Significance Statement

This study describes a data collocation (i.e., selection) process that aims to maximize the volume of data identified to be simultaneously collected in time and space from two coordinated measurement platforms. The functional utility of the resultant dataset is also demonstrated by extending the validation of aerosol particle number concentration derived from standard lidar and polarimeter data products from a suborbital mission that used two aircraft platforms.

Open access
Khadija Arjdal
,
Étienne Vignon
,
Fatima Driouech
,
Frédérique Chéruy
,
Salah Er-Raki
,
Adriana Sima
,
Abdelghani Chehbouni
, and
Philippe Drobinski

Abstract

Land surface–atmosphere interactions are a key component of climate modeling. They are particularly critical to understand and anticipate the climate and the water resources over the semiarid and arid North African regions. This study uses in situ observations to assess the ability of the IPSL-CM global climate model to simulate the land–atmosphere interactions over the Moroccan semiarid plains. A specific configuration with a grid refinement over the Haouz Plain, near Marrakech, and nudging outside Morocco has been performed to properly assess the model’s performances. To ensure reliable model–observation comparisons despite the fact that station measurements are not representative of a mesh-size area, we carried out experiments with adapted vegetation properties. Results show that the CMIP6 version of the model’s physics represents the near-surface climate over the Haouz Plain reasonably well. Nonetheless, the simulation exhibits a nocturnal warm bias, and the wind speed is overestimated in tree-covered meshes and underestimated in the wheat-covered region. Further sensitivity experiments reveal that LAI-dependent parameterization of roughness length leads to a strong surface wind drag and to underestimated land surface atmosphere thermal coupling. Setting the roughness heights to the observed values improves the wind speed and, to a lesser extent, the nocturnal temperature. A low bias in latent heat flux and soil moisture coinciding with a pronounced diurnal warm bias at the surface is still present in our simulations. Including a first-order irrigation parameterization yields more realistic simulated evapotranspiration flux and daytime skin surface temperatures. This result raises the importance of accounting for the irrigation process in present and future climate simulations over Moroccan agricultural areas.

Restricted access
L. J. Gelinas
,
J. H. Hecht
, and
R. J. Rudy

Abstract

The OH airglow layer is a persistent feature of Earth’s upper mesosphere, centered near 87 km altitude, that can be perturbed by atmospheric gravity waves (AGWs) and instabilities. While ground-based airglow imaging has been used to study these perturbations locally, this technique is limited by tropospheric weather. Space-based remote sensing provides a platform to measure these processes globally. In addition, portions of the OH airglow band span an atmospheric window, allowing airglow illumination of the ground for imaging of nighttime clouds and Earth terrain features. The Near-Infrared Airglow Camera (NIRAC) images the airglow at 1.6 μm and while deployed to the International Space Station (ISS) from May 2019 to November 2021 demonstrated these applications. The camera uses a patented motion-compensation system with a custom rectilinear lens that allows multisecond, nearly smear-free imaging (∼<1.5 pixels) at a ground pixel resolution of ∼83 m. With a ∼170 km × 170 km ground swath, NIRAC acquires overlapping images at a 7–10-s cadence. Parallax considerations enable detection of both AGWs and instabilities in the airglow, and scenes can be analyzed for terrain and cloud height. NIRAC also has a short-exposure daytime mode for cloud and ground imagery. This study describes NIRAC and its operations on the ISS and presents imagery examples of Earth terrain and surface phenomenology (such as fires), cloud imagery at all moon phases day and night, and the nighttime detection of AGWs and instabilities above 80 km altitude.

Significance Statement

The Near-Infrared Airglow Camera (NIRAC) is the first space-based instrument to exploit the bright 1.6 μm OH Meinel airglow emission band for Earth surface imager at resolution of ∼83 m. During its 2.5-yr deployment on the International Space Station (ISS), NIRAC obtained over a half million images of Earth’s surface and OH airglow layer. NIRAC has been able to capture images of the very small-scale (<30 km) AGWs and instabilities under a wide range of viewing conditions, including (i) in the vicinity of city lights, (ii) over complex cloud scenes, and (iii) under both moondown and moonup illumination. NIRAC also acquired daytime and nighttime images of clouds, hurricanes and typhoons, human lighting, and forest fires in the 1.6 μm band.

Restricted access
Naoyuki Kurita
,
Takao Kameda
,
Hideaki Motoyama
,
Naohiko Hirasawa
,
David Mikolajczyk
,
Lee J. Welhouse
,
Linda M. Keller
,
George A. Weidner
, and
Matthew A. Lazzara

Abstract

The interior of Dronning Maud Land (DML) in East Antarctica is one of the most data-sparse regions of Antarctica for studying climate change. A monthly mean near-surface temperature dataset for the last 30 years has been compiled from the historical records from automatic weather stations (AWSs) at three sites in the region (Mizuho, Relay Station, and Dome Fuji). Multiple AWSs have been installed along the route to Dome Fuji since the 1990s, and observations have continued to the present day. The use of passive-ventilated radiation shields for the temperature sensors at the AWSs may have caused a warm bias in the temperature measurements, however, due to insufficient ventilation in the summer, when solar radiation is high and winds are low. In this study, these warm biases are quantified by comparison with temperature measurements with an aspirated shield and subsequently removed using a regression model. Systematic error resulting from changes in the sensor height due to accumulating snow was insignificant in our study area. Several other systematic errors occurring in the early days of the AWS systems were identified and corrected. After the corrections, multiple AWS records were integrated to create a time series for each station. The percentage of missing data over the three decades was 21% for Relay Station and 28% for Dome Fuji. The missing rate at Mizuho was 49%, more than double that at Relay Station. These new records allow for the study of temperature variability and change in DML, where climate change has so far been largely unexplored.

Significance Statement

Antarctic climate change has been studied using temperature data at staffed stations. The staffed stations, however, are mainly located on the Antarctic Peninsula and in the coastal regions. Climate change is largely unknown in the Antarctic plateau, particularly in the western sector of the East Antarctic Plateau in areas such as the interior of Dronning Maud Land (DML). To fill the data gap, this study presents a new dataset of monthly mean near-surface climate data using historical observations from three automatic weather stations (AWSs). This dataset allows us to study temperature variability and change over a data-sparse region where climate change has been largely unexplored.

Restricted access
Erica L. McGrath-Spangler
,
N. C. Privé
,
Bryan M. Karpowicz
,
Isaac Moradi
, and
Andrew K. Heidinger

Abstract

The Geostationary eXtended Observations (GeoXO) program plans to include a hyperspectral infrared (IR) sounder on its central satellite, expected to launch in the mid-2030s. As part of the follow-on to the GOES program, the NOAA/NASA GeoXO Sounder (GXS) instrument will join several international counterparts in a geostationary orbit. In preparation, the NASA Global Modeling and Assimilation Office (GMAO) assessed the potential effectiveness of GXS both as a single GEO IR sounder and as part of a global ring that includes international partners. Using a global observing system simulation experiment (OSSE) framework, GXS was assessed from a numerical weather prediction (NWP) perspective. Evaluation of the ability of GXS, both alone and as part of a global ring ofGEOsounders, to improveweather prediction of thermodynamic variables was performed globally and regionally. GXS dominated regional analysis and forecast improvements, and contributed significantly to global increases in forecast skill relative to a Control. However, more sustained global improvements, on the order of 4 days, relied on international partnerships. Additionally, GXS showed the capability to improve hurricane forecast track errors on the timescales necessary for evacuation warnings. The FSOI metric over CONUS showed that the GXS observations provided the largest radiance impact on the moist energy error norm reduction. The high temporal resolution atmospheric profile information over much of the western hemisphere from GXS provides an opportunity to improve the representation of weather systems and their forecasts.

Restricted access
Mahsa Payami
,
Yunsoo Choi
,
Ahmed Khan Salman
,
Seyedali Mousavinezhad
,
Jincheol Park
, and
Arman Pouyaei

Abstract

In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a 1-dimensional Convolutional Neural Network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorology, and land use land cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an Index of Agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal 3-fold cross-validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley Additive Explanations analysis. The results revealed solar radiation reaching the surface, Planetary Boundary Layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times faster in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well.

Open access