Enhancing historical climate model data using super-resolution technology

Like video and image enhancements, machine learning can improve model resolution. Credit: Ruian Tie

Super-resolution technology is a new computing method used to enhance older meteorological model data so that scientists can better assess Earth’s global climate history. Upscaling digital photos and videos super-resolution calculations are an important analysis tool to calculate historical high-resolution model assimilation data, according to Dr. Chunxiang Shi, Chief Scientist at the National Meteorological Information Center of China Meteorological Administration.

“Due to the sparse historical observation data, the China Meteorological Administration land data assimilation system (CLDAS) cannot generate high-quality and high-resolution data,” said Dr. Shi. “At the beginning of last year, I learned that super-resolution technology can be used to complete high-resolution reconstruction of videos and pictures. We can also integrate this technology into reconstructing high-resolution historical assimilation data.”

Dr. Shi and her team from the National Meteorological Information Center of China Meteorological Administration are also known for CMA’s Land Data Assimilation System (CLDAS) and China’s 40-year global atmospheric/land surface reanalysis dataset (CRA-40). Recently, they published their super-resolution downscaling research based on CLDAS data in Advances in Atmospheric Sciences.

Specifically, the team built a downscaling model CLDASSD (CLDAS Statistical Downscaling). Using 2m temperature model data within the Beijing-Tianjin-Hebei region, researchers performed their downscaling test, making large-scale (low resolution) model output available to enhance local scale forecasts (high resolution). Their method successfully reconstructed fine textures in complex mountain areas, where human observation may be impossible. Through comparison with , the root mean square error of CLDASSD is smaller than the general interpolation-based downscaling methods used with different daily times, seasons, and terrain.

“Natural images and meteorological data have similarities in some respects, some computer vision techniques (Super-resolution, semantic segmentation, etc.) may be applied in atmosphere.” said Dr. Shi. “In the future, we will learn from even better super-resolution technologies to upgrade our model and carry out more experiments using , 10m wind, precipitation, etc. elements throughout China to fill the gap in CLDAS.”

More information:
Ruian Tie et al, CLDASSD: Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology, Advances in Atmospheric Sciences (2022). DOI: 10.1007/s00376-021-0438-y

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Hexbyte Glen Cove Enhancing quantum dot solar cell efficiency to 11.53% thumbnail

Hexbyte Glen Cove Enhancing quantum dot solar cell efficiency to 11.53%

Hexbyte Glen Cove

Figure 1. Shown above is the structure of CQDSC and the optical redistribution profiles of devices by TMF optical simulation. Credit: Professor Sung-Yeon Jang, UNIST

A novel technology that can improve the efficiency of quantum dot solar cells to 11.53% has been unveiled. Published in the February 2020 issue of Advanced Energy Materials, it has been evaluated as a study that solved the challenges posed by the generation of electric currents from sunlight by solar cells by enhancing the hole extraction.

A research team, led by Professor Sung-Yeon Jang in the School of Energy and Chemical Engineering at UNIST has developed a that maximizes the performance of quantum dot solar by using .

Solar cells use a characteristic of which electrons and holes are generated in the absorber layer. The free free electrons and hole then move through the cell, creating and filling in holes. It is this movement of electrons and holes that generate electricity. Therefore, creating multiple and transporting them are an important consideration in the design of efficient solar cells.

The research team switched one side of the quantum dot solar cells to organic hole transport materials (HTMs) to better extract and transport holes. This is because the newly-developed organic polymer not only possesses superior hole extracting ability, but also prevents electrons and holes from recombining, which allow efficient transport of holes to the anode.

Generally, quantum dot solar cells combine electron-rich quantum dots (n-type CQDs) and hole-rich quantum dots (p-type QDs). In this work, the research team developed organic π‐conjugated polymer (π‐CP) based HTMs, which can achieve performance superior to that of state‐of‐the‐art HTM, p‐type CQDs. The molecular engineering of the π‐CPs alters their optoelectronic properties, and the charge generation and collection in colloidal quantum dot solar cells (CQDSCs), using them are substantially improved.

As a result, the research team succeeded in achieving power conversion efficiency (PCE) of 11.53% with decent air‐storage stability. This is the highest reported PCE among CQDSCs using organic HTMs, and even higher than the reported best solid‐state ligand exchange‐free CQDSC using pCQD‐HTM. “From the viewpoint of device processing, device fabrication does not require any solid‐state ligand exchange step or layer‐by‐layer deposition process, which is favorable for exploiting commercial processing techniques,” noted the research team.

“This study solves the problem of hole transport, which has been the major obstacle for the genration of electric currents in quantum dot ,” says Professor Jang. “This work suggests that the molecular engineering of organic π‐CPs is an efficient strategy for simultaneous improvement in PCE and processability of CQDSCs, and additional optimization might further improve their performance.”

More information:
Muhibullah Al Mubarok et al. Molecular Engineering in Hole Transport π‐Conjugated Polymers to Enable High Efficiency Colloidal Quantum Dot Solar Cells, Advanced Energy Materials (2020). DOI: 10.1002/aenm.201902933

Enhancing quantum dot solar cell efficiency to 11.53% (2020, November 20)
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