Optimal estimation of sea surface temperature from AMSR-E

The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST

Globala egenskaper i samband med NAO-indexet

The past 51 years northern Europe has experienced nine unusually cold winters, where many temperature records have been set. Two of the three last winters, 2009/10 and 2010/11, were the coldest in many places in southern Sweden since the mid 80’s. However, the coldest winter in southern Sweden was, according to SMHI (Sveriges Meteorologiska och Hydrologiska Institut), the winter of 1962/63, when it on average was about 2˚C colder than the winter of 2009/10. For the winters of 2009/10 and 2010/11 the NAO-index was strongly negative during the major part of the cold period. This lead to studies of the index and during the winters of 2009/10 and 2010/11 a possible correlation between the NAO-index and the total surface pressure of the Northern hemisphere was found. Because of this, this report will be dedicated to studying if the other seven unusually cold winters in northern Europe show proof of a similar correlation. The analysis focuses, based on the NAO-index, the total surface pressure and the temperature as well as a number of other parameters, on determining if the correlation is a recurring phenomenon or not for the unusually cold winters in northern Europe. Based on the analysis, clear indications that a correlation between the NAO-index and the total surface pressure of the Northern hemisphere can be seen.De senaste 51 åren har norra Europa upplevt nio ovanligt kalla vintrar där många temperaturrekord har satts. Två av de tre senaste vintrarna, 2009/10 och 2010/11, var de kallaste på många håll i södra Sverige sedan mitten av 80-talet. Den allra kallaste vintern i södra Sverige var dock enligt SMHI (Sveriges Meteorologiska och Hydrologiska Institut) vintern 1962/63 då det i genomsnitt var cirka 2˚C kallare än vintern 2009/10. För både vintrarna 2009/10 och 2010/11 var NAO-indexet kraftigt negativt under största delen av den kalla perioden. Detta ledde till undersökningar av indexet och under vintrarna 2009/10 och 2010/11 upptäcktes en möjlig korrelation mellan NAO-indexet och det totala yttrycket på norra hemisfären. På grund av detta ägnas denna rapport åt att studera ifall de sju andra ovanligt kalla vintrarna i norra Europa uppvisar samma sorts korrelation. Analysen fokuserar på att utifrån NAO-indexet, det totala yttrycket och temperaturen samt ett antal andra parametrar, fastställa ifall korrelationen är ett återkommande fenomen eller inte för de ovanligt kalla vintrarna i norra Europa. Utifrån analysen som görs ses tydliga indikationer på att en korrelation mellan NAO-indexet och det totala yttrycket på norra hemisfären finns

Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements

Two machine learning (ML) models are investigated for retrieving sea surface temperature (SST) from passive microwave (PMW) satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and auxiliary data, such as ERA5 reanalysis data. The first model is the Extreme Gradient Boosting (XBG) model and the second is a multilayer perceptron neural network (NN). The performance of the two ML algorithms is compared to that of an existing state-of-the-art regression (RE) retrieval algorithm. The performance of the three algorithms is assessed using independent in situ SSTs from drifting buoys. Overall, the three models have similar biases; 0.01, 0.01 and −0.02 K for the XGB, NN and RE, respectively. The XGB model performs best with respect to standard deviation; 0.36 K. While the NN model performs slightly better than the RE model with respect to standard deviation, 0.50 and 0.55 K, respectively, the RE model is found to be more sensitive to changes in the in situ SST. Moreover, the XGB model is the least sensitive with an overall sensitivity of 0.78, compared to 0.90 for the RE model and 0.88 for the NN model. The good performance of the two ML algorithms compared to the state-of-the-art RE algorithm in this initial study demonstrates that there is a large potential in the use of ML algorithms for the retrieval of SST from PMW satellite observations

Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements

Two machine learning (ML) models are investigated for retrieving sea surface temperature (SST) from passive microwave (PMW) satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and auxiliary data, such as ERA5 reanalysis data. The first model is the Extreme Gradient Boosting (XBG) model and the second is a multilayer perceptron neural network (NN). The performance of the two ML algorithms is compared to that of an existing state-of-the-art regression (RE) retrieval algorithm. The performance of the three algorithms is assessed using independent in situ SSTs from drifting buoys. Overall, the three models have similar biases; 0.01, 0.01 and −0.02 K for the XGB, NN and RE, respectively. The XGB model performs best with respect to standard deviation; 0.36 K. While the NN model performs slightly better than the RE model with respect to standard deviation, 0.50 and 0.55 K, respectively, the RE model is found to be more sensitive to changes in the in situ SST. Moreover, the XGB model is the least sensitive with an overall sensitivity of 0.78, compared to 0.90 for the RE model and 0.88 for the NN model. The good performance of the two ML algorithms compared to the state-of-the-art RE algorithm in this initial study demonstrates that there is a large potential in the use of ML algorithms for the retrieval of SST from PMW satellite observations