CondiDiag1.0: A flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)

Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy exchanges with the Earth's surface. In order to identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding and large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. This paper presents an online diagnostic tool that addresses this challenge by monitoring model variables in a generic manner as they evolve within the time integration cycle. The tool is convenient to use. It allows users to select sampling conditions and specify monitored variables at run time. Both the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived. Online calculation of vertical integrals is also supported. Multiple sampling conditions can be monitored in a single simulation in combination with unconditional sampling. The paper explains in detail the design and implementation of the tool in the Energy Exascale Earth System Model (E3SM) version 1. The usage is demonstrated through three examples: a global budget analysis of dust aerosol mass concentration, a composite analysis of sea salt emission and its dependency on surface wind speed, and a conditionally sampled relative humidity budget. The tool is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods.

Large-Eddy Simulation for the Roughness Sublayers over Real Urban Surfaces

<p><strong>ABSTRACT: </strong></p><p>    With the continuous spreading of global pandemic, environmental issues have aroused worldwide unprecedented attention. Airflow plays a crucial role in aerosol motions and pollutants removal in dense cities. Large-eddy simulation (LES) is conducted for a typical metropolitan, Hong Kong, to investigate the dynamics in the atmospheric boundary layer (ABL) over real urban surfaces. Full-scale building models (average building height h<sub>m</sub> = 36 m) from Tsim Sha Tsui to Sham Shui Po, Kowloon Peninsula, are digitalized. Southerly wind with speed U<sub>∞</sub> (= 10 m sec<sup>-1</sup>) in neutral stratification is prescribed at the domain inlet. The turbulence statistics extracted from three subdomains in Mong Kok neighborhood, each with size 800 m (streamwise) × 100 m (spanwise) × 500 m (vertical), are analyzed. Linear regression of the wind profile with the logarithmic law of the wall (log-law) show that the interface between inertial sublayer (ISL) and roughness sublayer (RSL) is in the range of 2.5h<sub>m</sub> to 4.5h<sub>m</sub>. In the RSL, the streamwise and vertical velocities are positively (S<sub>u</sub> > 0) and negatively (S<sub>w</sub> < 0) skewed, respectively. Their kurtosis K<sub>u</sub> and K<sub>w</sub> is less than 3. Conditional sampling of vertical momentum, flux u’’w’’ showed that ejection Q2 occurs more frequently than does sweep Q4. On the contrary, the contribution of Q4 exceeds that of Q2. These characteristics switch to the other way round in the ISL. Furthermore, the difference between Q4 and Q2, either in terms of occurrence or contribution, shows a local maximum around 50% of the total momentum flux, suggesting the major energy-carrying scales. Coherent structures depict elongated, (massive,) accelerating (decelerating) and descending (ascending) RSL (ISL) flows. Hence, the fresh (aged) air entrainment (detrainment) are signified by fast and extreme (slow and frequent) flows. These distinct features of RSL flows over real urban morphology provide an inspiration to improve the ground-level air quality by proper urban planning.</p><p><strong>KEYWORDS:</strong> Large-eddy simulation (LES), real urban morphology, turbulent boundary layer (TBL), conditional sampling, hole filtering</p><p> </p><p> </p>

PERSEPSI MASYARAKAT TERHADAP PENGOBATAN TRADISIONAL BERDASARKAN PERBEDAAN JENIS KELAMIN

Latar Belakang: Masyarakat di Indonesia khususnya masyarakat yang bertempat tinggal di kabupaten Purwakarta secara turun temurun telah menggunakan ramuan tradisional sebagai alternatif pengobatan, dan akhir-akhir ini semakin marak produk-produk yang berbahan herbal di pasaran. Pengguna obat tradisional ini mencakup berbagai kalangan mulai dari anak-anak sampai orang dewasa. Maraknya penggunaan obat tradisional di masyarakat, tidak sejalan dengan tersedia data mengenai siapa yang merupakan konsumen paling tinggi dan bagaimana dampaknya terhadap kesehatan mereka. Tujuan Penelitian: Mengeksplorasi persepsi masyarakat terkait pengobatan tradisional berdasarkan perbedaan Jenis Kelamin. Metode: Metode yang digunakan dalam penelitian ini yaitu eksplanatif asosiatif. Subjek penelitian adalah masyarakat Kabupaten Purwakarta Propinsi Jawa Barat yang berjumlah 137 orang. Pengambilan sampel dilakukan dengan menggunakan metode conditional sampling. Instrumen penelitian berubah lembar observasi. Hasil: Hasil penelitian menunjukkan bahwa dari 137 responden memiliki pengetahuan dan animo terhadap pengobatan tradisional (cukup: 58,3%), percaya pengobatan tradisional memiliki potensi dikembangkan sebagai upaya kesehatan (kuat: 61,5%), percaya upaya pengobatan kesehatan dapat menyembuhkan (cukup: 52,6%), percaya bahwa fasilitas praktik pengobatan tradisional belum terstandarisasi (cukup: 54,3%). Simpulan: Kelompok responden wanita dibandingkan terhadap kelompok pria memiliki persepsi lebih rendah terhadap potensi pengembangan pengobatan tradisional, memiliki tingkat kepercayaan lebih rendah pada kemampuan penyembuhan dari pengobatan tradisional, memiliki tingkat kepercayaan yang lebih tinggi bahwa pengobatan tradisional belum terstandarisasi, namun relatif memiliki persepsi yang sama sesuai pengetahuan dan animo terhadap pengobatan tradisional.

Wall-Shear-Stress-Based Conditional Sampling Analysis of Coherent Structures in a Turbulent Boundary Layer

Coherent structures are critical for controlling turbulent boundary layers due to their roles in momentum and heat transfer in the flow. Turbulent coherent structures can be detected by measuring wall shear stresses that are footprints of coherent structures. In this study, wall shear stress fluctuations were measured simultaneously in a zero pressure gradient turbulent boundary layer using two house-made wall shear stress probes aligned in the spanwise direction. The wall shear stress probe consisted of two hot-wires on the wall aligned in a V-shaped configuration for measuring streamwise and spanwise shear stresses, and their performance was validated in comparison with a direct numerical simulation result. Relationships between measured wall shear stress fluctuations and streamwise velocity fluctuations were analyzed using conditional sampling techniques. The peak detection method and the variable-interval time-averaging (VITA) method showed that quasi-streamwise vortices were inclined toward the streamwise direction. When events were simultaneously detected by the two probes, stronger fluctuations in streamwise velocity were detected, which suggests that stronger coherent structures were detected. In contrast to the former two methods, the hibernating event detection method detects events with lower wall shear stress fluctuations. The ensemble-averaged mean velocity profile of hibernating events was shifted upward compared to the law of the wall, which suggests low drag status of the coherent structures related with hibernating events. These methods suggest significant correlations between wall shear stress fluctuations and coherent structures, which could motivate flow control strategies to fully exploit these correlations.

On Senders’s Models of Visual Sampling Behavior

Objective We review the sampling models described in John Senders’s doctoral thesis on “visual sampling processes” via a ready and accessible exposition. Background John Senders left a significant imprint on human factors/ergonomics (HF/E). Here, we focus on one preeminent aspect of his career, namely visual attention. Methods We present, clarify, and expand the models in his thesis through computer simulation and associated visual illustrations. Results One of the key findings of Senders’s work on visual sampling concerns the linear relationship between signal bandwidth and visual sampling rate. The models that are used to describe this relationship are the periodic sampling model (PSM), the random constrained sampling model (RCM), and the conditional sampling model (CSM). A recent replication study that used results from modern eye-tracking equipment showed that Senders’s original findings are manifestly replicable. Conclusions Senders’s insights and findings withstand the test of time and his models continue to be both relevant and useful to the present and promise continued impact in the future. Application The present paper is directed to stimulate a broad spectrum of researchers and practitioners in HF/E and beyond to use these important and insightful models.

Can Short and Partial Observations Reduce Model Error and Facilitate Machine Learning Prediction?

Predicting complex nonlinear turbulent dynamical systems is an important and practical topic. However, due to the lack of a complete understanding of nature, the ubiquitous model error may greatly affect the prediction performance. Machine learning algorithms can overcome the model error, but they are often impeded by inadequate and partial observations in predicting nature. In this article, an efficient and dynamically consistent conditional sampling algorithm is developed, which incorporates the conditional path-wise temporal dependence into a two-step forward-backward data assimilation procedure to sample multiple distinct nonlinear time series conditioned on short and partial observations using an imperfect model. The resulting sampled trajectories succeed in reducing the model error and greatly enrich the training data set for machine learning forecasts. For a rich class of nonlinear and non-Gaussian systems, the conditional sampling is carried out by solving a simple stochastic differential equation, which is computationally efficient and accurate. The sampling algorithm is applied to create massive training data of multiscale compressible shallow water flows from highly nonlinear and indirect observations. The resulting machine learning prediction significantly outweighs the imperfect model forecast. The sampling algorithm also facilitates the machine learning forecast of a highly non-Gaussian climate phenomenon using extremely short observations.