在“电脑如何预测空气质量？ ”一文里，我已经为大家描述了空气质量模式是怎样把大气层分割成一个个方形的格子来计算和预测每个格子内的空气质量的。我们知道，空气质量模式预测的空气污染物浓度是具有一定分辨率的，即在每个方格子内，污染物浓度是均一的，而这个方格子的边长一般至少是几公里甚至更长的距离，比如矮马预报目前采用的12公里边长。那么这样由电脑“计算”出来的空气质量靠谱吗？
在回答这个问题以前，我们首先要明确“靠谱”是什么含义。我们讨论的是预测出来的空气质量是否靠谱，那么很明显这是相对真实的空气质量来说的，而真实的空气质量只能通过实测来获得。也就是说“靠谱”的含义是，计算机的预测值和监测网络的实测值是否吻合。当然，这里还有一个实测值是否靠谱的问题，即实测值是否反应了“真实值”。这里我们不进一步讨论实测的不确定性问题，有兴趣的读者请参考“什么是空气质量监测网络”一文。也就是说，我们的问题是计算机的预测和监测网络的实测相比一致吗？通常的误差又是多少？
与经过严格质量控制的实测数据进行比对，目前国际上最靠谱的空气质量模式的预报准确性，是对日最大8小时臭氧的预测误差为平均20%左右，对PM2.5日均值的预报误差为平均30%左右。这个20%或者30%的误差，是一个平均值，指的是预测浓度值和实测浓度值之间的差值除以实测浓度值的百分比的长期平均值。这个误差对单天来讲，有时会很大，比如会达到70-80%，有时会很小，比如零误差。注意，这里我们不能说准确性为80%或者70%。通常媒体上出现的说法，预报准确性70%或80%指的是空气质量等级的预报准确性，即有70%的天数，预报的污染等级和实测的污染等级是一致的。而实际上，如果预测AQI是40，实测AQI是20，两者的等级都是优，但是数值误差却高达100%。当然也有相反的情况，比如预测是AQI为140，但是实测是AQI为160，这时预测为轻度污染，而实测为中度污染，是完全不同的等级 ，但是数值误差却只有12.5%。那么如何理解这个20%或者30%的误差，这些误差又都来自那些因素呢？
首先，如何理解这个20%或者30%的预测误差？在学术界看来，这个误差已经很小了。甚至在短期内如果不发生理论上的根本性革命的话，有可能这就是空气质量模式预测水平的极限了。也因为这个原因，西方发达国家在利用空气质量模式或称法规模式进行区域性污染控制规划时，对效验模式的准确性要求的规定就是依据这样的预测误差水平来制定的，其对具体误差数值的要求甚至比20%或者30%还要宽松。然而，我们可以迅速地知道，这个30%的相对误差，对300微克每立方米的实测PM2.5浓度，意味着将近100微克每立方米的绝对误差。因此，空气质量模式预测发生等级上的错判（比如从轻度污染到中度污染）是不足为奇的。
那么这些预测误差主要来自哪些因素，又可以怎样去克服这些误差呢？
计算机利用数值模式来预测空气质量本身是有数值误差的，见“人工预报和数值预报”一文。这个数值误差，是人类无法对描述空气质量的数学方程组进行解析求解造成的，只能用数值方法求解近似值。这个数值误差是没有办法完全弥补的，这也是人类无法准确进行中长期的天气和空气质量预报的根本原因。
另外，空气质量模式作为格子模式，用覆盖面积很大的格子的平均浓度去模拟或者对应一个点上的实测浓度，本身也是一种理论上的缺陷。克服这个理论缺陷的方法，一是对监测点位的选址要有严格的要求，即要求其具有一定的空间代表性。监测点位周边必须视野开阔，空气交流充分，没有临近的污染源排放点等（具体见“什么是空气质量监测网络”一文）。这个选址的要求是要保证实测值至少能够代表点位周边几公里甚至数十公里的空气质量平均状况。然而，随着实测污染物种的不同，这个站点实测的空间代表性是很不相同的。对于一次污染物，即直接排放的物种，比如一氧化碳，其空间代表性就差，原因是一氧化碳的排放在空间上的不均匀性直接会造成其浓度分布上的空间不均匀性。而对于二次污染物，即完全由一次污染物发生化学转化而来的物种，比如臭氧，其站点空间代表性要好很多。因为需要一次污染物排放后，经过均匀混合，才能经由复杂的化学反应而最后生成。PM2.5则较为复杂，因为PM2.5成分差异很大，有一部分是属于一次的范畴，也有更多的部分是二次成分。另一方面，空气质量模式也可以通过不断提高空间分辨来提高预测数值与站点实测值的可比性。比如，将计算格子分割得更小一些，如4公里边长的方格子。但是这里有一个计算资源的限制问题。无限细分目前不被大多数的硬件条件允许。同时参数化理论上也有限制，其极限大概是略小于1公里边长的格子。鉴于计算资源的限制问题，科学家也想出了其他办法来克服这个空间分辨率的问题。这就是变网格的设计，也叫自适应网格。就是在污染物浓度均匀的地方允许大格子，在浓度梯度大的地方使用加密的的细格子，这样在保持计算格子总数的情况下提高了预测的空间分辨率。这里的格子可以不再限制为方格子，可以是任意4边形，甚至是5边形的格子。
除了以上的系统误差以外，主要还有三个方面的因素会造成空气质量模式的预测误差。一是气象条件的预测误差。二是源排放量的预测误差。三是空气质量模式的理论描述误差，也就是对污染过程的科学认识不准确的地方。气象条件的预测误差对空气质量模式的预测是影响最大的，因为气象条件预测得不准，空气质量预测发生很大误差的可能性最大，这时源排放预测再准，理论描述再对也没有用。比如实际有大风过程，但是没有预测出来，或者实际有暴雨天气发生，但是没有预测出来。好在当前短期天气预报系统的准确性已经非常的好了，当前5天的预报水平已经和30年前3天的预报水平基本相当。矮马预报也得益于这个进步，在大多数情况下对天气系统转换的把握都基本没有问题。但是这并不是说100%的准确率。当一些极端天气形势发生时，短期天气预报的预报能力是需要进一步接受考验的。而每当这种误判发生时，空气质量预报也是不可能准确的。而在气象要素预测得相对较好的时候，源排放的不确定性则是主要问题，理论描述的误差影响并没有那么显著。尤其是在中国，源清单的编制存在很多问题的情况下。矮马预报出现较大偏差时，我们会首先查看气象条件的预报是否有问题，然后再考虑源排放的原因。但是多数情况下，以上三个因素都会对空气质量的预测误差有贡献。因此，矮马预报的系统维护努力主要就是针对这三个方面来进行的。我们分别针对这三个方面采取相应的各种技术手段以维持预测准确性。而其中最多的努力放在源排放估算和预测上，原因是我国这方面的数据支持比较缺乏，再加上经济发展迅猛造成的源排放日新月异。源排放估算和预测方面的努力，不仅包括最新数据和资料的搜集，也包括各种经济、能源预测技术和源排放反演技术的应用（参见“什么是源排放清单？”一文）。
基于我们对空气质量数值预报误差的深刻认识，矮马预报还将通过下面两方面的努力来进一步减小其预测的不确定性。一是开展矮马专家预报。我们正在招募志愿者，参加各个城市的空气质量人工预报或称矮马专家预报。空气质量数值预报很难做到每一个城市、每一片区域、每一天都完全准确，因此我们希望早日实现矮马专家预报（见矮马博客“人工预报和数值预报”一文），用人工经验对空气质量模式的预报数值进行合理的校证。一是利用化学物种同化技术，即把实时观测到的PM2.5和其他空气污染物浓度通过数学方法同化进数值结果，从而提高预报准确性。矮马预报的理想是开发一种小型传感器，可以由普通公众在自己的手机上附带安装一个这样的传感器并测量自身呼吸的空气质量，同时为矮马实时发回空气质量监测数据，我们称之为“我为矮马测空气”。这个志愿行动参与的人越多，可以时时刻刻为矮马提供越多的全国范围的实测空气质量信息，从而帮助矮马回馈给大家一个更为准确的数值预报产品。
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