Pressure-Level-Guided Spectral Cross-Attention for AIRS Temperature Profile Retrieval over East Asia Under Clear-Sky Summer Conditions

Conventional neural retrieval models commonly use a single spectral representation to predict all pressure levels, limiting their ability to capture altitude-dependent information. We propose a Pressure-Conditioned Cross-Attention Network (PC-CAN) for temperature-profile retrieval from Atmospheric Infrared Sounder (AIRS) Level 1C radiances. A three-stage one-dimensional residual encoder and adaptive tokenizer convert each 2645-channel spectrum into 64 spectral tokens with 64 features each. Continuous log-pressure encoding generates queries for 37 standard levels, and four-head cross-attention constructs level-specific features for a shared regression head. A screened regional dataset of 319,194 AIRS matches with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) was constructed over 15–55° N and 70–140° E using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud masks, summer observations, and date-independent partitions. On 84,618 test samples, PC-CAN achieved an RMSE of 1.3277 K, a bias of 0.1358 K, and a Pearson correlation coefficient of 0.9990 over 100–900 hPa, with an RMSE of 1.3935 K across all 37 pressure levels. Its 100–900 hPa RMSE was 16.66%, 18.90%, and 17.94% lower than those of a multilayer perceptron, a one-dimensional convolutional neural network, and an optimized spectral self-attention model, respectively. On an external set of 208 strictly collocated AIRS–Integrated Global Radiosonde Archive (IGRA) profiles from 70 stations, PC-CAN obtained an RMSE of 2.039 K, compared with 2.185–2.223 K for the three baselines; station-clustered paired bootstrap intervals supported the overall improvements. These results demonstrate improved agreement with ERA5 and radiosonde observations for clear-sky summer observations over East Asia and adjacent regions; they do not establish global, year-round, or cloudy-sky generalization.